{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "YOLOv5-Custom-Training.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/adrian-soch/frontier_exploration/blob/main/learned_frontier_detector/training/yolov5-custom-training-256.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNveqeA1KXGy"
      },
      "source": [
        "# Step 1: Install Requirements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTvDNSILZoN9",
        "outputId": "3c88535f-b310-4c44-d492-e82ea7250eff"
      },
      "source": [
        "#clone YOLOv5 and \n",
        "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
        "%cd yolov5\n",
        "%pip install -qr requirements.txt # install dependencies\n",
        "%pip install -q roboflow\n",
        "\n",
        "import torch\n",
        "import os\n",
        "from IPython.display import Image, clear_output  # to display images\n",
        "\n",
        "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov5'...\n",
            "remote: Enumerating objects: 15393, done.\u001b[K\n",
            "remote: Counting objects: 100% (24/24), done.\u001b[K\n",
            "remote: Compressing objects: 100% (18/18), done.\u001b[K\n",
            "remote: Total 15393 (delta 9), reused 16 (delta 6), pack-reused 15369\u001b[K\n",
            "Receiving objects: 100% (15393/15393), 14.37 MiB | 22.54 MiB/s, done.\n",
            "Resolving deltas: 100% (10519/10519), done.\n",
            "/content/yolov5\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m184.3/184.3 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.8/55.8 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.8/67.8 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Setup complete. Using torch 2.0.0+cu118 (Tesla T4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FwJcaoPGF4VI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7635f1d8-a08a-470c-f7a6-d288fc708f83"
      },
      "source": [
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"O6vsPUJcFqHX0o6PWJ4m\")\n",
        "project = rf.workspace(\"cas726\").project(\"learned-frontier-detection\")\n",
        "dataset = project.version(4).download(\"yolov5\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: roboflow in /usr/local/lib/python3.9/dist-packages (1.0.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.9/dist-packages (from roboflow) (2.8.2)\n",
            "Requirement already satisfied: pyparsing==2.4.7 in /usr/local/lib/python3.9/dist-packages (from roboflow) (2.4.7)\n",
            "Requirement already satisfied: cycler==0.10.0 in /usr/local/lib/python3.9/dist-packages (from roboflow) (0.10.0)\n",
            "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.9/dist-packages (from roboflow) (1.0.0)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.9/dist-packages (from roboflow) (1.22.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from roboflow) (2.27.1)\n",
            "Requirement already satisfied: requests-toolbelt in /usr/local/lib/python3.9/dist-packages (from roboflow) (0.10.1)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.9/dist-packages (from roboflow) (8.4.0)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.9/dist-packages (from roboflow) (4.65.0)\n",
            "Requirement already satisfied: certifi==2022.12.7 in /usr/local/lib/python3.9/dist-packages (from roboflow) (2022.12.7)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.9/dist-packages (from roboflow) (6.0)\n",
            "Requirement already satisfied: idna==2.10 in /usr/local/lib/python3.9/dist-packages (from roboflow) (2.10)\n",
            "Requirement already satisfied: chardet==4.0.0 in /usr/local/lib/python3.9/dist-packages (from roboflow) (4.0.0)\n",
            "Requirement already satisfied: opencv-python>=4.1.2 in /usr/local/lib/python3.9/dist-packages (from roboflow) (4.7.0.72)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from roboflow) (1.16.0)\n",
            "Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.9/dist-packages (from roboflow) (1.26.15)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.9/dist-packages (from roboflow) (1.4.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (from roboflow) (3.7.1)\n",
            "Requirement already satisfied: wget in /usr/local/lib/python3.9/dist-packages (from roboflow) (3.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->roboflow) (23.0)\n",
            "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->roboflow) (5.12.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->roboflow) (4.39.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->roboflow) (1.0.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->roboflow) (2.0.12)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources>=3.2.0->matplotlib->roboflow) (3.15.0)\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Downloading Dataset Version Zip in Learned-Frontier-Detection-4 to yolov5pytorch: 100% [946186 / 946186] bytes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting Dataset Version Zip to Learned-Frontier-Detection-4 in yolov5pytorch:: 100%|██████████| 186/186 [00:00<00:00, 2565.46it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7yAi9hd-T4B"
      },
      "source": [
        "# Step 3: Train Our Custom YOLOv5 model\n",
        "\n",
        "Here, we are able to pass a number of arguments:\n",
        "- **img:** define input image size\n",
        "- **batch:** determine batch size\n",
        "- **epochs:** define the number of training epochs. (Note: often, 3000+ are common here!)\n",
        "- **data:** Our dataset locaiton is saved in the `dataset.location`\n",
        "- **weights:** specify a path to weights to start transfer learning from. Here we choose the generic COCO pretrained checkpoint.\n",
        "- **cache:** cache images for faster training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaFNnxLJbq4J",
        "outputId": "35d36e42-1c15-42bc-dc28-dfa5b09534c7"
      },
      "source": [
        "IMG_SIZE = 256\n",
        "!python train.py --img {IMG_SIZE} --batch 32 --epochs 333 --data {dataset.location}/data.yaml --weights yolov5n.pt --cache --freeze 10"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5n.pt, cfg=, data=/content/yolov5/Learned-Frontier-Detection-4/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=333, batch_size=32, imgsz=256, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[10], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 v7.0-140-g1db9533 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
            "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
            "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100% 755k/755k [00:00<00:00, 46.2MB/s]\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt to yolov5n.pt...\n",
            "100% 3.87M/3.87M [00:00<00:00, 9.36MB/s]\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=1\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      1760  models.common.Conv                      [3, 16, 6, 2, 2]              \n",
            "  1                -1  1      4672  models.common.Conv                      [16, 32, 3, 2]                \n",
            "  2                -1  1      4800  models.common.C3                        [32, 32, 1]                   \n",
            "  3                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  4                -1  2     29184  models.common.C3                        [64, 64, 2]                   \n",
            "  5                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  6                -1  3    156928  models.common.C3                        [128, 128, 3]                 \n",
            "  7                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  8                -1  1    296448  models.common.C3                        [256, 256, 1]                 \n",
            "  9                -1  1    164608  models.common.SPPF                      [256, 256, 5]                 \n",
            " 10                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 13                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
            " 14                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
            " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     22912  models.common.C3                        [128, 64, 1, False]           \n",
            " 18                -1  1     36992  models.common.Conv                      [64, 64, 3, 2]                \n",
            " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
            " 20                -1  1     74496  models.common.C3                        [128, 128, 1, False]          \n",
            " 21                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
            " 24      [17, 20, 23]  1      8118  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [64, 128, 256]]\n",
            "Model summary: 214 layers, 1765270 parameters, 1765270 gradients, 4.2 GFLOPs\n",
            "\n",
            "Transferred 343/349 items from yolov5n.pt\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
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            "freezing model.8.m.0.cv2.bn.weight\n",
            "freezing model.8.m.0.cv2.bn.bias\n",
            "freezing model.9.cv1.conv.weight\n",
            "freezing model.9.cv1.bn.weight\n",
            "freezing model.9.cv1.bn.bias\n",
            "freezing model.9.cv2.conv.weight\n",
            "freezing model.9.cv2.bn.weight\n",
            "freezing model.9.cv2.bn.bias\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/yolov5/Learned-Frontier-Detection-4/train/labels... 75 images, 0 backgrounds, 0 corrupt: 100% 75/75 [00:00<00:00, 1566.19it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/yolov5/Learned-Frontier-Detection-4/train/labels.cache\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.0GB ram): 100% 75/75 [00:00<00:00, 1096.89it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/yolov5/Learned-Frontier-Detection-4/valid/labels... 6 images, 1 backgrounds, 0 corrupt: 100% 6/6 [00:00<00:00, 430.35it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/yolov5/Learned-Frontier-Detection-4/valid/labels.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB ram): 100% 6/6 [00:00<00:00, 519.97it/s]\n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.66 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Plotting labels to runs/train/exp/labels.jpg... \n",
            "Image sizes 256 train, 256 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
            "Starting training for 333 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      0/332     0.352G     0.1217    0.03046          0         72        256: 100% 3/3 [00:05<00:00,  1.70s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:01<00:00,  1.67s/it]\n",
            "                   all          6         25    0.00444       0.32    0.00346    0.00136\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      1/332     0.354G      0.122    0.03333          0         91        256: 100% 3/3 [00:00<00:00,  8.29it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.64it/s]\n",
            "                   all          6         25    0.00389       0.28    0.00321    0.00146\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      2/332     0.354G     0.1221    0.03411          0        120        256: 100% 3/3 [00:00<00:00,  8.01it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.49it/s]\n",
            "                   all          6         25    0.00444       0.32    0.00354    0.00154\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      3/332     0.354G     0.1198    0.03279          0         90        256: 100% 3/3 [00:00<00:00,  7.81it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.75it/s]\n",
            "                   all          6         25    0.00611       0.44    0.00589    0.00233\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      4/332     0.354G     0.1159    0.03671          0         83        256: 100% 3/3 [00:00<00:00,  5.32it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.14it/s]\n",
            "                   all          6         25    0.00778       0.56    0.00842    0.00303\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      5/332     0.354G      0.115    0.03502          0         80        256: 100% 3/3 [00:00<00:00,  5.14it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.83it/s]\n",
            "                   all          6         25    0.00943        0.4    0.00953    0.00323\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      6/332     0.354G     0.1129    0.03836          0         96        256: 100% 3/3 [00:00<00:00,  5.69it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.07it/s]\n",
            "                   all          6         25     0.0111       0.36     0.0109    0.00374\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      7/332     0.354G       0.11    0.03905          0         81        256: 100% 3/3 [00:00<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.45it/s]\n",
            "                   all          6         25    0.00667       0.48     0.0122    0.00483\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      8/332     0.354G      0.107    0.04036          0        101        256: 100% 3/3 [00:00<00:00,  9.36it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.37it/s]\n",
            "                   all          6         25    0.00722       0.52     0.0145    0.00597\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      9/332     0.354G     0.1047    0.04298          0        107        256: 100% 3/3 [00:00<00:00,  9.61it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.44it/s]\n",
            "                   all          6         25    0.00778       0.56     0.0182    0.00707\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     10/332     0.354G     0.1029    0.03942          0         64        256: 100% 3/3 [00:00<00:00,  8.41it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.74it/s]\n",
            "                   all          6         25     0.0362       0.32     0.0256     0.0108\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     11/332     0.354G     0.1007    0.04114          0         72        256: 100% 3/3 [00:00<00:00,  9.17it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.37it/s]\n",
            "                   all          6         25     0.0771       0.12     0.0568     0.0209\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     12/332     0.354G    0.09966    0.04717          0        111        256: 100% 3/3 [00:00<00:00,  9.25it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.78it/s]\n",
            "                   all          6         25      0.122       0.16      0.106     0.0303\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     13/332     0.354G     0.0966    0.04398          0         98        256: 100% 3/3 [00:00<00:00,  9.05it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.57it/s]\n",
            "                   all          6         25       0.15       0.16      0.146     0.0651\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     14/332     0.354G    0.09421    0.04454          0         93        256: 100% 3/3 [00:00<00:00,  9.22it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.99it/s]\n",
            "                   all          6         25      0.301       0.16      0.142     0.0758\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     15/332     0.354G    0.09091    0.04432          0         92        256: 100% 3/3 [00:00<00:00,  8.17it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.17it/s]\n",
            "                   all          6         25      0.301       0.24      0.172      0.046\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     16/332     0.354G     0.0891    0.04608          0         98        256: 100% 3/3 [00:00<00:00,  8.56it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.79it/s]\n",
            "                   all          6         25        0.4       0.32      0.234     0.0587\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     17/332     0.354G    0.08889    0.04274          0         76        256: 100% 3/3 [00:00<00:00,  9.65it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.29it/s]\n",
            "                   all          6         25      0.258       0.24       0.14     0.0382\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     18/332     0.357G    0.08742    0.04631          0         82        256: 100% 3/3 [00:00<00:00,  7.04it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.82it/s]\n",
            "                   all          6         25       0.12       0.28        0.1     0.0396\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     19/332     0.357G    0.08248    0.04445          0         70        256: 100% 3/3 [00:00<00:00,  5.34it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.50it/s]\n",
            "                   all          6         25       0.25       0.28      0.145     0.0544\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     20/332     0.357G    0.08392    0.04612          0        127        256: 100% 3/3 [00:00<00:00,  6.94it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.46it/s]\n",
            "                   all          6         25      0.353      0.328      0.183     0.0607\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     21/332     0.357G    0.07941    0.04457          0         98        256: 100% 3/3 [00:00<00:00,  5.29it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.55it/s]\n",
            "                   all          6         25      0.454       0.32       0.22     0.0867\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     22/332     0.357G    0.07813    0.04551          0         78        256: 100% 3/3 [00:00<00:00,  8.72it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.43it/s]\n",
            "                   all          6         25      0.327      0.292       0.16     0.0528\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     23/332     0.357G    0.07752    0.04794          0         99        256: 100% 3/3 [00:00<00:00,  8.73it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.29it/s]\n",
            "                   all          6         25      0.294        0.2      0.132     0.0322\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     24/332     0.357G    0.07831    0.04504          0         85        256: 100% 3/3 [00:00<00:00, 10.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.92it/s]\n",
            "                   all          6         25      0.264       0.24      0.173     0.0329\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     25/332     0.357G    0.07858    0.04418          0         88        256: 100% 3/3 [00:00<00:00, 12.14it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.65it/s]\n",
            "                   all          6         25       0.19        0.4      0.157     0.0569\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     26/332     0.357G    0.07608    0.04525          0        117        256: 100% 3/3 [00:00<00:00, 13.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.44it/s]\n",
            "                   all          6         25      0.117       0.44     0.0777     0.0242\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     27/332     0.357G    0.07649    0.04722          0        102        256: 100% 3/3 [00:00<00:00,  6.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.77it/s]\n",
            "                   all          6         25      0.153       0.36      0.101     0.0321\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     28/332     0.357G    0.07309    0.04875          0        123        256: 100% 3/3 [00:00<00:00,  9.89it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.43it/s]\n",
            "                   all          6         25      0.234        0.4      0.153     0.0413\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     29/332     0.357G    0.07061    0.04428          0         94        256: 100% 3/3 [00:00<00:00, 10.01it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.59it/s]\n",
            "                   all          6         25      0.342        0.4      0.238     0.0821\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     30/332     0.357G    0.07126    0.04349          0         66        256: 100% 3/3 [00:00<00:00, 10.12it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.60it/s]\n",
            "                   all          6         25      0.153        0.4       0.11     0.0348\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     31/332     0.357G     0.0809    0.04058          0        106        256: 100% 3/3 [00:00<00:00,  8.15it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.96it/s]\n",
            "                   all          6         25      0.312        0.4      0.271     0.0789\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     32/332     0.357G    0.06894     0.0429          0         98        256: 100% 3/3 [00:00<00:00, 13.52it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.66it/s]\n",
            "                   all          6         25      0.224       0.36        0.2     0.0682\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     33/332     0.357G    0.06618    0.04556          0         92        256: 100% 3/3 [00:00<00:00, 10.13it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.04it/s]\n",
            "                   all          6         25      0.257        0.4      0.302     0.0765\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     34/332     0.357G    0.06813    0.04405          0         84        256: 100% 3/3 [00:00<00:00,  8.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.43it/s]\n",
            "                   all          6         25      0.275      0.471      0.255     0.0717\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     35/332     0.357G    0.06196    0.04222          0         87        256: 100% 3/3 [00:00<00:00,  7.29it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.50it/s]\n",
            "                   all          6         25      0.218        0.6       0.28     0.0645\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     36/332     0.357G    0.06673    0.04341          0         90        256: 100% 3/3 [00:00<00:00,  7.04it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  4.85it/s]\n",
            "                   all          6         25      0.266       0.56      0.305     0.0947\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     37/332     0.357G    0.06087     0.0446          0         84        256: 100% 3/3 [00:00<00:00,  9.70it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.40it/s]\n",
            "                   all          6         25      0.316       0.48      0.332      0.116\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     38/332     0.357G    0.06594    0.04581          0        120        256: 100% 3/3 [00:00<00:00, 10.20it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.61it/s]\n",
            "                   all          6         25      0.205        0.4      0.245     0.0696\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     39/332     0.357G    0.06203    0.04114          0         71        256: 100% 3/3 [00:00<00:00, 10.15it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.13it/s]\n",
            "                   all          6         25       0.35       0.28      0.345     0.0965\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     40/332     0.357G    0.06471    0.03904          0         72        256: 100% 3/3 [00:00<00:00, 10.37it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.38it/s]\n",
            "                   all          6         25      0.344        0.4      0.333      0.135\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     41/332     0.357G    0.05872    0.04467          0         94        256: 100% 3/3 [00:00<00:00,  9.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.35it/s]\n",
            "                   all          6         25      0.454       0.56      0.422      0.156\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     42/332     0.357G    0.06008    0.04109          0         85        256: 100% 3/3 [00:00<00:00, 11.40it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.07it/s]\n",
            "                   all          6         25      0.293       0.52      0.303      0.106\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     43/332     0.357G    0.05595    0.03925          0         78        256: 100% 3/3 [00:00<00:00,  8.25it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.09it/s]\n",
            "                   all          6         25      0.443       0.32       0.34      0.124\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     44/332     0.357G    0.06341     0.0404          0         90        256: 100% 3/3 [00:00<00:00, 11.09it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.02it/s]\n",
            "                   all          6         25       0.41       0.52      0.456      0.155\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     45/332     0.357G    0.05358    0.04325          0         92        256: 100% 3/3 [00:00<00:00,  8.02it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.02it/s]\n",
            "                   all          6         25      0.362        0.4      0.324      0.115\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     46/332     0.357G    0.05936    0.04284          0         94        256: 100% 3/3 [00:00<00:00,  9.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.73it/s]\n",
            "                   all          6         25      0.554       0.36       0.41      0.147\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     47/332     0.357G     0.0543     0.0422          0         90        256: 100% 3/3 [00:00<00:00,  6.95it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.49it/s]\n",
            "                   all          6         25      0.374       0.36      0.306      0.143\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     48/332     0.357G    0.05908    0.04432          0        106        256: 100% 3/3 [00:00<00:00,  6.29it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.95it/s]\n",
            "                   all          6         25      0.423       0.44      0.431      0.189\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     49/332     0.357G    0.05516    0.04281          0         76        256: 100% 3/3 [00:00<00:00,  4.89it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.18it/s]\n",
            "                   all          6         25      0.313       0.48      0.337      0.122\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     50/332     0.357G    0.05477    0.04179          0        111        256: 100% 3/3 [00:00<00:00,  5.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.94it/s]\n",
            "                   all          6         25       0.26       0.68      0.392      0.124\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     51/332     0.357G    0.05302    0.03926          0         82        256: 100% 3/3 [00:00<00:00,  6.91it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.82it/s]\n",
            "                   all          6         25       0.46       0.44      0.448      0.162\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     52/332     0.357G    0.05721    0.04403          0         99        256: 100% 3/3 [00:00<00:00,  7.18it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.93it/s]\n",
            "                   all          6         25       0.38        0.6       0.51      0.211\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     53/332     0.357G    0.05399    0.04652          0        130        256: 100% 3/3 [00:00<00:00,  9.72it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.15it/s]\n",
            "                   all          6         25      0.586       0.48      0.556      0.239\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     54/332     0.357G    0.05303    0.04064          0         92        256: 100% 3/3 [00:00<00:00, 10.50it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.48it/s]\n",
            "                   all          6         25      0.258      0.736       0.41      0.166\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     55/332     0.357G     0.0517    0.04309          0         86        256: 100% 3/3 [00:00<00:00, 11.16it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.14it/s]\n",
            "                   all          6         25      0.647       0.44      0.537      0.248\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     56/332     0.357G    0.05233     0.0417          0         82        256: 100% 3/3 [00:00<00:00,  9.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.63it/s]\n",
            "                   all          6         25      0.384        0.4      0.311      0.125\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     57/332     0.357G    0.05792    0.04221          0         95        256: 100% 3/3 [00:00<00:00,  9.36it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.85it/s]\n",
            "                   all          6         25      0.449       0.44      0.334       0.15\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     58/332     0.357G    0.05304    0.03988          0         66        256: 100% 3/3 [00:00<00:00, 10.74it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.11it/s]\n",
            "                   all          6         25      0.543        0.4      0.415      0.176\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     59/332     0.357G    0.04818    0.04038          0         87        256: 100% 3/3 [00:00<00:00,  8.98it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.30it/s]\n",
            "                   all          6         25      0.796       0.44      0.604      0.265\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     60/332     0.357G    0.05149     0.0459          0        144        256: 100% 3/3 [00:00<00:00,  9.28it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.95it/s]\n",
            "                   all          6         25      0.593      0.466      0.564      0.219\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     61/332     0.357G    0.04878    0.04383          0        100        256: 100% 3/3 [00:00<00:00, 11.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.84it/s]\n",
            "                   all          6         25      0.564       0.57      0.561      0.254\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     62/332     0.357G    0.04748    0.03861          0         85        256: 100% 3/3 [00:00<00:00, 12.78it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.49it/s]\n",
            "                   all          6         25      0.631        0.4      0.462      0.194\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     63/332     0.357G    0.04854    0.04041          0         68        256: 100% 3/3 [00:00<00:00,  8.43it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.62it/s]\n",
            "                   all          6         25      0.605        0.8      0.686      0.292\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     64/332     0.357G    0.05138    0.04528          0        124        256: 100% 3/3 [00:00<00:00, 14.60it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.35it/s]\n",
            "                   all          6         25      0.396        0.8      0.487      0.164\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     65/332     0.357G    0.05308    0.04642          0        107        256: 100% 3/3 [00:00<00:00,  5.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.57it/s]\n",
            "                   all          6         25      0.351       0.76       0.38      0.152\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     66/332     0.357G    0.05464    0.03939          0         80        256: 100% 3/3 [00:00<00:00,  6.71it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.97it/s]\n",
            "                   all          6         25      0.554       0.72      0.486      0.242\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     67/332     0.357G    0.04572    0.04241          0        120        256: 100% 3/3 [00:00<00:00,  7.11it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.03it/s]\n",
            "                   all          6         25      0.462       0.64      0.553      0.263\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     68/332     0.357G    0.05195    0.04197          0        112        256: 100% 3/3 [00:00<00:00,  6.73it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.76it/s]\n",
            "                   all          6         25      0.374      0.644      0.428      0.194\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     69/332     0.357G     0.0479    0.04196          0        105        256: 100% 3/3 [00:00<00:00,  9.52it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.33it/s]\n",
            "                   all          6         25       0.43       0.72      0.531      0.236\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     70/332     0.357G    0.04907    0.04069          0         86        256: 100% 3/3 [00:00<00:00,  9.00it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.42it/s]\n",
            "                   all          6         25      0.359        0.6       0.45      0.153\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     71/332     0.357G    0.04755    0.03609          0         67        256: 100% 3/3 [00:00<00:00,  9.38it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.52it/s]\n",
            "                   all          6         25      0.584       0.52      0.613      0.261\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     72/332     0.357G    0.05024    0.04395          0         98        256: 100% 3/3 [00:00<00:00, 10.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.99it/s]\n",
            "                   all          6         25      0.467      0.773      0.638      0.282\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     73/332     0.357G    0.04534    0.04198          0         99        256: 100% 3/3 [00:00<00:00,  8.16it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.39it/s]\n",
            "                   all          6         25      0.624       0.64      0.651        0.3\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     74/332     0.357G    0.04726    0.04023          0         98        256: 100% 3/3 [00:00<00:00, 10.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.60it/s]\n",
            "                   all          6         25      0.556       0.72      0.674      0.277\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     75/332     0.357G    0.04699    0.04066          0         90        256: 100% 3/3 [00:00<00:00,  9.99it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.71it/s]\n",
            "                   all          6         25      0.528       0.76      0.687      0.287\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     76/332     0.357G    0.04816    0.04433          0        114        256: 100% 3/3 [00:00<00:00,  9.53it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.65it/s]\n",
            "                   all          6         25      0.605      0.553      0.623      0.225\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     77/332     0.357G    0.04699    0.04166          0        105        256: 100% 3/3 [00:00<00:00,  8.74it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.02it/s]\n",
            "                   all          6         25       0.66        0.7      0.706       0.31\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     78/332     0.357G     0.0486    0.03968          0         89        256: 100% 3/3 [00:00<00:00, 11.32it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.59it/s]\n",
            "                   all          6         25      0.431       0.72       0.61      0.235\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     79/332     0.357G    0.04684    0.03708          0         80        256: 100% 3/3 [00:00<00:00,  9.41it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.56it/s]\n",
            "                   all          6         25      0.622       0.56      0.575      0.242\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     80/332     0.357G    0.04758    0.03528          0         69        256: 100% 3/3 [00:00<00:00, 10.60it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.45it/s]\n",
            "                   all          6         25      0.717       0.56      0.695      0.317\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     81/332     0.357G     0.0443    0.04086          0         94        256: 100% 3/3 [00:00<00:00,  5.12it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.77it/s]\n",
            "                   all          6         25      0.686      0.613      0.692      0.335\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     82/332     0.357G    0.04498    0.03944          0         93        256: 100% 3/3 [00:00<00:00,  5.71it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.74it/s]\n",
            "                   all          6         25      0.504       0.64      0.547      0.258\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     83/332     0.357G    0.04544    0.04054          0        110        256: 100% 3/3 [00:00<00:00,  6.17it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.25it/s]\n",
            "                   all          6         25      0.603        0.6      0.629      0.295\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     84/332     0.357G    0.04479    0.04038          0         83        256: 100% 3/3 [00:00<00:00,  9.93it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.63it/s]\n",
            "                   all          6         25      0.469        0.6      0.542      0.233\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     85/332     0.357G    0.04564     0.0427          0        115        256: 100% 3/3 [00:00<00:00,  9.64it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.66it/s]\n",
            "                   all          6         25      0.596       0.64      0.668       0.31\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     86/332     0.357G    0.04748    0.03992          0        102        256: 100% 3/3 [00:00<00:00, 11.12it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.25it/s]\n",
            "                   all          6         25        0.5      0.681      0.513       0.24\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     87/332     0.357G    0.04461     0.0398          0         84        256: 100% 3/3 [00:00<00:00,  9.10it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.62it/s]\n",
            "                   all          6         25      0.611       0.72      0.711      0.338\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     88/332     0.357G    0.04727    0.03967          0         97        256: 100% 3/3 [00:00<00:00,  8.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.17it/s]\n",
            "                   all          6         25      0.554       0.64      0.571      0.252\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     89/332     0.357G    0.04334    0.04397          0        113        256: 100% 3/3 [00:00<00:00,  8.42it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.65it/s]\n",
            "                   all          6         25      0.732       0.56      0.693      0.298\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     90/332     0.357G    0.04586    0.04225          0         85        256: 100% 3/3 [00:00<00:00, 11.48it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.03it/s]\n",
            "                   all          6         25      0.566       0.56      0.499      0.235\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     91/332     0.357G    0.04143    0.03642          0         77        256: 100% 3/3 [00:00<00:00,  9.41it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.72it/s]\n",
            "                   all          6         25      0.679       0.72      0.737      0.308\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     92/332     0.357G    0.04361    0.03916          0         84        256: 100% 3/3 [00:00<00:00, 10.11it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.66it/s]\n",
            "                   all          6         25      0.649        0.6       0.75      0.307\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     93/332     0.357G    0.04396    0.04009          0         81        256: 100% 3/3 [00:00<00:00,  9.37it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.37it/s]\n",
            "                   all          6         25      0.669       0.76      0.724      0.337\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     94/332     0.357G    0.04181    0.03971          0         85        256: 100% 3/3 [00:00<00:00,  9.65it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.02it/s]\n",
            "                   all          6         25      0.525      0.753      0.663      0.297\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     95/332     0.357G    0.04222    0.04226          0        107        256: 100% 3/3 [00:00<00:00,  9.83it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.03it/s]\n",
            "                   all          6         25      0.642       0.76      0.734      0.356\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     96/332     0.357G    0.04176    0.03672          0         77        256: 100% 3/3 [00:00<00:00, 11.18it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.60it/s]\n",
            "                   all          6         25      0.483       0.76      0.652      0.322\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     97/332     0.357G    0.04274     0.0403          0        101        256: 100% 3/3 [00:00<00:00,  6.43it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.69it/s]\n",
            "                   all          6         25      0.652       0.75      0.753      0.388\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     98/332     0.357G    0.04225    0.03803          0         88        256: 100% 3/3 [00:00<00:00,  6.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.80it/s]\n",
            "                   all          6         25      0.499        0.8      0.668      0.356\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     99/332     0.357G    0.04365    0.03652          0         82        256: 100% 3/3 [00:00<00:00,  4.90it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.09it/s]\n",
            "                   all          6         25      0.672       0.64      0.722      0.366\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    100/332     0.357G    0.04384    0.03858          0         86        256: 100% 3/3 [00:00<00:00,  8.53it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.92it/s]\n",
            "                   all          6         25      0.777      0.558      0.703      0.335\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    101/332     0.357G    0.04119    0.04066          0         97        256: 100% 3/3 [00:00<00:00,  9.27it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.42it/s]\n",
            "                   all          6         25      0.544        0.8      0.711      0.366\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    102/332     0.357G    0.04288    0.03811          0         91        256: 100% 3/3 [00:00<00:00,  8.66it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.45it/s]\n",
            "                   all          6         25      0.593      0.818      0.715      0.289\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    103/332     0.357G    0.04393    0.04143          0        100        256: 100% 3/3 [00:00<00:00,  9.58it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.34it/s]\n",
            "                   all          6         25      0.599        0.8      0.722      0.314\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    104/332     0.357G    0.04172    0.03704          0         85        256: 100% 3/3 [00:00<00:00, 12.44it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.25it/s]\n",
            "                   all          6         25       0.54        0.8      0.692      0.301\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    105/332     0.357G     0.0458    0.04225          0        114        256: 100% 3/3 [00:00<00:00, 11.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.96it/s]\n",
            "                   all          6         25      0.708       0.68      0.719      0.325\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    106/332     0.357G    0.04202    0.04027          0         96        256: 100% 3/3 [00:00<00:00, 10.97it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.77it/s]\n",
            "                   all          6         25      0.607       0.84      0.699      0.279\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    107/332     0.357G    0.04164    0.04114          0        108        256: 100% 3/3 [00:00<00:00,  9.39it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.00it/s]\n",
            "                   all          6         25      0.613        0.8      0.772      0.373\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    108/332     0.357G    0.04489    0.04052          0        101        256: 100% 3/3 [00:00<00:00, 10.63it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.52it/s]\n",
            "                   all          6         25      0.553      0.793       0.62      0.281\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    109/332     0.357G     0.0429    0.04075          0        107        256: 100% 3/3 [00:00<00:00, 12.45it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.69it/s]\n",
            "                   all          6         25      0.715      0.704      0.752      0.403\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    110/332     0.357G    0.04416    0.03825          0         86        256: 100% 3/3 [00:00<00:00,  8.34it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.66it/s]\n",
            "                   all          6         25      0.705       0.64      0.716      0.336\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    111/332     0.357G    0.04006    0.04019          0        112        256: 100% 3/3 [00:00<00:00,  8.92it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.95it/s]\n",
            "                   all          6         25      0.731       0.68      0.732      0.392\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    112/332     0.357G    0.04338    0.03714          0         90        256: 100% 3/3 [00:00<00:00, 10.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.76it/s]\n",
            "                   all          6         25      0.734      0.663      0.735      0.384\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    113/332     0.357G    0.04076    0.03835          0         90        256: 100% 3/3 [00:00<00:00,  5.72it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.38it/s]\n",
            "                   all          6         25      0.703       0.68      0.787      0.377\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    114/332     0.357G    0.04225    0.04061          0        102        256: 100% 3/3 [00:00<00:00,  5.85it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.83it/s]\n",
            "                   all          6         25      0.704       0.68      0.762      0.386\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    115/332     0.357G    0.03916     0.0394          0        113        256: 100% 3/3 [00:00<00:00,  6.94it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.42it/s]\n",
            "                   all          6         25      0.704       0.68      0.759       0.42\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    116/332     0.357G     0.0409    0.03709          0         79        256: 100% 3/3 [00:00<00:00,  8.98it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.20it/s]\n",
            "                   all          6         25      0.636       0.64      0.713      0.375\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    117/332     0.357G    0.04135    0.03982          0         77        256: 100% 3/3 [00:00<00:00, 11.76it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.96it/s]\n",
            "                   all          6         25      0.771      0.673      0.772      0.428\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    118/332     0.357G    0.04067    0.03941          0         83        256: 100% 3/3 [00:00<00:00, 14.10it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.19it/s]\n",
            "                   all          6         25      0.652      0.599      0.621      0.357\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    119/332     0.357G    0.03797    0.03671          0         89        256: 100% 3/3 [00:00<00:00,  8.17it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.63it/s]\n",
            "                   all          6         25      0.762       0.64      0.754      0.423\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    120/332     0.357G    0.03997    0.03721          0         73        256: 100% 3/3 [00:00<00:00,  9.56it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.24it/s]\n",
            "                   all          6         25      0.735      0.667      0.748      0.372\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    121/332     0.357G    0.03903     0.0367          0         79        256: 100% 3/3 [00:00<00:00,  9.44it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.37it/s]\n",
            "                   all          6         25      0.702       0.68      0.765      0.408\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    122/332     0.357G    0.03953    0.04014          0        110        256: 100% 3/3 [00:00<00:00,  9.28it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.88it/s]\n",
            "                   all          6         25      0.699       0.68      0.747      0.363\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    123/332     0.357G    0.03803    0.03903          0         84        256: 100% 3/3 [00:00<00:00,  5.99it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.28it/s]\n",
            "                   all          6         25      0.671      0.653      0.736      0.401\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    124/332     0.357G    0.04012    0.03695          0        102        256: 100% 3/3 [00:00<00:00,  7.89it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.26it/s]\n",
            "                   all          6         25      0.665       0.68      0.719      0.393\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    125/332     0.357G    0.03859    0.03609          0         78        256: 100% 3/3 [00:00<00:00,  4.73it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  4.56it/s]\n",
            "                   all          6         25      0.588       0.88      0.824       0.43\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    126/332     0.357G    0.04055    0.03986          0        114        256: 100% 3/3 [00:00<00:00, 11.03it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.14it/s]\n",
            "                   all          6         25      0.681       0.76      0.795      0.405\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    127/332     0.357G    0.04105    0.04039          0        111        256: 100% 3/3 [00:00<00:00,  5.72it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.87it/s]\n",
            "                   all          6         25      0.722      0.833      0.819      0.428\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    128/332     0.357G    0.03849    0.03695          0         98        256: 100% 3/3 [00:00<00:00,  6.62it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.17it/s]\n",
            "                   all          6         25      0.614      0.764      0.769       0.41\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    129/332     0.357G    0.03875    0.04084          0        104        256: 100% 3/3 [00:00<00:00,  7.77it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.25it/s]\n",
            "                   all          6         25      0.624        0.8       0.79      0.412\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    130/332     0.357G    0.03821    0.04042          0        110        256: 100% 3/3 [00:00<00:00,  9.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.96it/s]\n",
            "                   all          6         25      0.632       0.84      0.781      0.373\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    131/332     0.357G    0.03957    0.04053          0        104        256: 100% 3/3 [00:00<00:00,  9.69it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.42it/s]\n",
            "                   all          6         25      0.588        0.8      0.771      0.388\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    132/332     0.357G    0.03906    0.03642          0         98        256: 100% 3/3 [00:00<00:00, 11.01it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.55it/s]\n",
            "                   all          6         25      0.629       0.84      0.757      0.342\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    133/332     0.357G    0.04021    0.04113          0        112        256: 100% 3/3 [00:00<00:00,  9.33it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.28it/s]\n",
            "                   all          6         25      0.593      0.816      0.748      0.375\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    134/332     0.359G    0.04134    0.03723          0         95        256: 100% 3/3 [00:00<00:00,  9.62it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.11it/s]\n",
            "                   all          6         25       0.66      0.855      0.744      0.348\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    135/332     0.359G    0.04071    0.03881          0         90        256: 100% 3/3 [00:00<00:00,  9.98it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.07it/s]\n",
            "                   all          6         25      0.643       0.84       0.75      0.381\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    136/332     0.359G    0.03784    0.03758          0         93        256: 100% 3/3 [00:00<00:00, 11.52it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.56it/s]\n",
            "                   all          6         25      0.635       0.84      0.762      0.385\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    137/332     0.359G    0.03928    0.04014          0        101        256: 100% 3/3 [00:00<00:00,  8.92it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.82it/s]\n",
            "                   all          6         25      0.609       0.81      0.756      0.401\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    138/332     0.359G     0.0429    0.03845          0        104        256: 100% 3/3 [00:00<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.76it/s]\n",
            "                   all          6         25      0.595      0.882      0.758       0.41\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    139/332     0.359G    0.03865     0.0388          0         99        256: 100% 3/3 [00:00<00:00,  7.97it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.31it/s]\n",
            "                   all          6         25      0.614       0.92      0.767      0.389\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    140/332     0.359G    0.03947    0.03571          0         81        256: 100% 3/3 [00:00<00:00, 11.27it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.28it/s]\n",
            "                   all          6         25      0.617       0.88      0.763      0.389\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    141/332     0.359G    0.04087    0.03931          0         89        256: 100% 3/3 [00:00<00:00,  7.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.17it/s]\n",
            "                   all          6         25      0.667       0.92      0.785       0.42\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    142/332     0.359G    0.03728    0.03556          0         78        256: 100% 3/3 [00:00<00:00,  9.20it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.58it/s]\n",
            "                   all          6         25      0.631      0.891      0.778      0.393\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    143/332     0.359G     0.0377    0.03761          0        104        256: 100% 3/3 [00:00<00:00,  6.09it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.24it/s]\n",
            "                   all          6         25      0.619       0.92      0.797      0.407\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    144/332     0.359G    0.03669    0.03624          0         85        256: 100% 3/3 [00:00<00:00,  6.72it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.04it/s]\n",
            "                   all          6         25      0.741      0.687      0.784       0.39\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    145/332     0.359G    0.03747     0.0355          0         70        256: 100% 3/3 [00:00<00:00,  5.74it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.32it/s]\n",
            "                   all          6         25      0.703      0.759        0.8      0.385\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    146/332     0.359G    0.03907    0.03987          0         91        256: 100% 3/3 [00:00<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.17it/s]\n",
            "                   all          6         25      0.625        0.8      0.763      0.343\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    147/332     0.359G    0.03918    0.04007          0        108        256: 100% 3/3 [00:00<00:00,  8.91it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.67it/s]\n",
            "                   all          6         25       0.63       0.92      0.755      0.384\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    148/332     0.359G    0.04158    0.03988          0        103        256: 100% 3/3 [00:00<00:00,  9.90it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.07it/s]\n",
            "                   all          6         25      0.651       0.92      0.787      0.456\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    149/332     0.359G    0.03584    0.03377          0         66        256: 100% 3/3 [00:00<00:00, 10.09it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.50it/s]\n",
            "                   all          6         25      0.638       0.92      0.809      0.423\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    150/332     0.359G    0.04059    0.03751          0        107        256: 100% 3/3 [00:00<00:00, 10.63it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.23it/s]\n",
            "                   all          6         25      0.634      0.903      0.795      0.421\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    151/332     0.359G    0.03653    0.03635          0         78        256: 100% 3/3 [00:00<00:00,  9.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.50it/s]\n",
            "                   all          6         25      0.726      0.741      0.793      0.439\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    152/332     0.359G    0.03915    0.03846          0        102        256: 100% 3/3 [00:00<00:00, 11.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.69it/s]\n",
            "                   all          6         25      0.727       0.76      0.763      0.389\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    153/332     0.359G    0.03846    0.03781          0         93        256: 100% 3/3 [00:00<00:00,  7.72it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.24it/s]\n",
            "                   all          6         25      0.694       0.76      0.749      0.366\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    154/332     0.359G    0.03777    0.04021          0        124        256: 100% 3/3 [00:00<00:00,  9.11it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.15it/s]\n",
            "                   all          6         25        0.7       0.76      0.751      0.365\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    155/332     0.359G    0.03571    0.03603          0         99        256: 100% 3/3 [00:00<00:00,  7.74it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.01it/s]\n",
            "                   all          6         25      0.614       0.88      0.755      0.392\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    156/332     0.359G    0.03432    0.03534          0         89        256: 100% 3/3 [00:00<00:00, 10.62it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.65it/s]\n",
            "                   all          6         25      0.695      0.819      0.772      0.402\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    157/332     0.359G    0.03658    0.03917          0        102        256: 100% 3/3 [00:00<00:00,  9.11it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.68it/s]\n",
            "                   all          6         25      0.692       0.88      0.769      0.415\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    158/332     0.359G    0.03638    0.03741          0         88        256: 100% 3/3 [00:00<00:00,  7.37it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.90it/s]\n",
            "                   all          6         25      0.661       0.88      0.763      0.415\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    159/332     0.359G    0.03753    0.03866          0         98        256: 100% 3/3 [00:00<00:00,  6.31it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.58it/s]\n",
            "                   all          6         25      0.715       0.84      0.785       0.44\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    160/332     0.359G    0.03558    0.03789          0        100        256: 100% 3/3 [00:00<00:00,  6.74it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.79it/s]\n",
            "                   all          6         25      0.711        0.8      0.784      0.414\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    161/332     0.359G    0.03763    0.03817          0         94        256: 100% 3/3 [00:00<00:00,  8.83it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.40it/s]\n",
            "                   all          6         25      0.699      0.837      0.767       0.42\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    162/332     0.359G    0.03702    0.03754          0         89        256: 100% 3/3 [00:00<00:00,  9.15it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.78it/s]\n",
            "                   all          6         25      0.655       0.84      0.784      0.417\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    163/332     0.359G    0.03859    0.03904          0         97        256: 100% 3/3 [00:00<00:00,  9.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.13it/s]\n",
            "                   all          6         25      0.677      0.839      0.779      0.427\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    164/332     0.359G    0.03678    0.03899          0         95        256: 100% 3/3 [00:00<00:00,  9.40it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.21it/s]\n",
            "                   all          6         25      0.655       0.84       0.75      0.403\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    165/332     0.359G    0.03587    0.03859          0        107        256: 100% 3/3 [00:00<00:00,  9.33it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.83it/s]\n",
            "                   all          6         25      0.663       0.84      0.768       0.37\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    166/332     0.359G    0.03572    0.03695          0         99        256: 100% 3/3 [00:00<00:00, 11.10it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.39it/s]\n",
            "                   all          6         25      0.627       0.84      0.764        0.4\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    167/332     0.359G    0.03718    0.03659          0         83        256: 100% 3/3 [00:00<00:00,  9.26it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.73it/s]\n",
            "                   all          6         25       0.77       0.68      0.762      0.415\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    168/332     0.359G    0.03859    0.03665          0         94        256: 100% 3/3 [00:00<00:00,  9.52it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.82it/s]\n",
            "                   all          6         25      0.771      0.672       0.78      0.414\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    169/332     0.359G    0.03807    0.03757          0        108        256: 100% 3/3 [00:00<00:00,  8.95it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.99it/s]\n",
            "                   all          6         25      0.685        0.8      0.779      0.432\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    170/332     0.359G    0.03534    0.03735          0         82        256: 100% 3/3 [00:00<00:00,  8.66it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.63it/s]\n",
            "                   all          6         25      0.719       0.76      0.783      0.413\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    171/332     0.359G    0.03621    0.03924          0         92        256: 100% 3/3 [00:00<00:00,  9.24it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.86it/s]\n",
            "                   all          6         25       0.68       0.88      0.784      0.412\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    172/332     0.359G    0.03499    0.03265          0         57        256: 100% 3/3 [00:00<00:00,  8.56it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.87it/s]\n",
            "                   all          6         25      0.665       0.88      0.775      0.447\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    173/332     0.359G    0.03791    0.04028          0        134        256: 100% 3/3 [00:00<00:00,  9.05it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.42it/s]\n",
            "                   all          6         25      0.679       0.88      0.781      0.456\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    174/332     0.359G    0.03662    0.03663          0         94        256: 100% 3/3 [00:00<00:00,  6.64it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.35it/s]\n",
            "                   all          6         25      0.651       0.88      0.786       0.45\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    175/332     0.359G    0.03494    0.03744          0         84        256: 100% 3/3 [00:00<00:00, 10.28it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.54it/s]\n",
            "                   all          6         25      0.684       0.88      0.776      0.425\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    176/332     0.359G    0.03701    0.03667          0         85        256: 100% 3/3 [00:00<00:00,  6.81it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  4.50it/s]\n",
            "                   all          6         25      0.679       0.84      0.798      0.445\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    177/332     0.359G    0.03518    0.03695          0         89        256: 100% 3/3 [00:00<00:00,  9.75it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.17it/s]\n",
            "                   all          6         25      0.683       0.84      0.796      0.441\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    178/332     0.359G    0.03644    0.03725          0         97        256: 100% 3/3 [00:00<00:00, 10.10it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.30it/s]\n",
            "                   all          6         25      0.624       0.88      0.789      0.416\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    179/332     0.359G    0.03759     0.0402          0         99        256: 100% 3/3 [00:00<00:00, 10.41it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.98it/s]\n",
            "                   all          6         25      0.671       0.84      0.782      0.411\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    180/332     0.359G    0.03375    0.03456          0         83        256: 100% 3/3 [00:00<00:00, 12.67it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.91it/s]\n",
            "                   all          6         25      0.695        0.8      0.778      0.407\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    181/332     0.359G    0.03381     0.0388          0        109        256: 100% 3/3 [00:00<00:00,  8.65it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.79it/s]\n",
            "                   all          6         25       0.73      0.865      0.784      0.426\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    182/332     0.359G    0.03742    0.03944          0        101        256: 100% 3/3 [00:00<00:00, 10.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.61it/s]\n",
            "                   all          6         25      0.697      0.829      0.755      0.397\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    183/332     0.359G    0.03484    0.03583          0         81        256: 100% 3/3 [00:00<00:00,  9.27it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.74it/s]\n",
            "                   all          6         25      0.721       0.88      0.782      0.408\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    184/332     0.359G    0.03517     0.0387          0         91        256: 100% 3/3 [00:00<00:00,  8.65it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.45it/s]\n",
            "                   all          6         25       0.72       0.88      0.797       0.42\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    185/332     0.359G    0.03497     0.0354          0         77        256: 100% 3/3 [00:00<00:00,  9.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.10it/s]\n",
            "                   all          6         25      0.663       0.88      0.731      0.406\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    186/332     0.359G     0.0367    0.03509          0         73        256: 100% 3/3 [00:00<00:00, 10.95it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.62it/s]\n",
            "                   all          6         25      0.659       0.84      0.743        0.4\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    187/332     0.359G    0.03288    0.03605          0         80        256: 100% 3/3 [00:00<00:00, 12.34it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.76it/s]\n",
            "                   all          6         25      0.711       0.88      0.748      0.403\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    188/332     0.359G    0.03548    0.03602          0        105        256: 100% 3/3 [00:00<00:00,  8.06it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 13.12it/s]\n",
            "                   all          6         25      0.622        0.8      0.711      0.367\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    189/332     0.359G    0.03351     0.0372          0         94        256: 100% 3/3 [00:00<00:00,  9.11it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.34it/s]\n",
            "                   all          6         25      0.674      0.827       0.74      0.401\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    190/332     0.359G    0.03423    0.03709          0         77        256: 100% 3/3 [00:00<00:00,  7.76it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.25it/s]\n",
            "                   all          6         25      0.651        0.8      0.743      0.396\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    191/332     0.359G    0.03568    0.03724          0        102        256: 100% 3/3 [00:00<00:00,  5.64it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.13it/s]\n",
            "                   all          6         25      0.648       0.81      0.752      0.414\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    192/332     0.359G    0.03374    0.03636          0         82        256: 100% 3/3 [00:00<00:00,  7.01it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.40it/s]\n",
            "                   all          6         25      0.627       0.88      0.744       0.43\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    193/332     0.359G    0.03498    0.04057          0        105        256: 100% 3/3 [00:00<00:00,  9.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.03it/s]\n",
            "                   all          6         25      0.648       0.84      0.746      0.415\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    194/332     0.359G    0.03618    0.04231          0        140        256: 100% 3/3 [00:00<00:00, 11.03it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.04it/s]\n",
            "                   all          6         25      0.635        0.8      0.764      0.394\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    195/332     0.359G    0.03454    0.03804          0         85        256: 100% 3/3 [00:00<00:00,  9.17it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.77it/s]\n",
            "                   all          6         25      0.644       0.84      0.769      0.415\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    196/332     0.359G    0.03663    0.03669          0         93        256: 100% 3/3 [00:00<00:00,  9.42it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.09it/s]\n",
            "                   all          6         25       0.66      0.777      0.743      0.366\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    197/332     0.359G    0.03739    0.03712          0         95        256: 100% 3/3 [00:00<00:00, 10.22it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.87it/s]\n",
            "                   all          6         25       0.66       0.88      0.757      0.413\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    198/332     0.359G     0.0336    0.03459          0         88        256: 100% 3/3 [00:00<00:00,  5.80it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.80it/s]\n",
            "                   all          6         25      0.666       0.88      0.752      0.409\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    199/332     0.359G     0.0339    0.03814          0        108        256: 100% 3/3 [00:00<00:00,  4.88it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.20it/s]\n",
            "                   all          6         25      0.652       0.88      0.771      0.421\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    200/332     0.359G    0.03362    0.03405          0         79        256: 100% 3/3 [00:00<00:00,  6.47it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.81it/s]\n",
            "                   all          6         25      0.661       0.88      0.778      0.422\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    201/332     0.359G    0.03519    0.03964          0        119        256: 100% 3/3 [00:00<00:00,  6.89it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.08it/s]\n",
            "                   all          6         25      0.694       0.84      0.775      0.412\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    202/332     0.359G    0.03473     0.0364          0         99        256: 100% 3/3 [00:00<00:00,  7.89it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.37it/s]\n",
            "                   all          6         25      0.667       0.84      0.785      0.411\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    203/332     0.359G    0.03489    0.03803          0        107        256: 100% 3/3 [00:00<00:00,  9.44it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.34it/s]\n",
            "                   all          6         25       0.72       0.84        0.8      0.427\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    204/332     0.359G    0.03579    0.03795          0         76        256: 100% 3/3 [00:00<00:00,  5.92it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.12it/s]\n",
            "                   all          6         25       0.72       0.84      0.805      0.435\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    205/332     0.359G    0.03345    0.03539          0         89        256: 100% 3/3 [00:00<00:00,  5.86it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.86it/s]\n",
            "                   all          6         25      0.698       0.84      0.803      0.422\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    206/332     0.359G    0.03474    0.03775          0        113        256: 100% 3/3 [00:00<00:00,  7.27it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.30it/s]\n",
            "                   all          6         25      0.722      0.831      0.793      0.401\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    207/332     0.359G    0.03564    0.03369          0         59        256: 100% 3/3 [00:00<00:00,  8.61it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.05it/s]\n",
            "                   all          6         25      0.633       0.92      0.794      0.405\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    208/332     0.359G    0.03556    0.03723          0         87        256: 100% 3/3 [00:00<00:00, 10.36it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.70it/s]\n",
            "                   all          6         25       0.67       0.84      0.796      0.399\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    209/332     0.359G    0.03572    0.03298          0         57        256: 100% 3/3 [00:00<00:00,  9.88it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.02it/s]\n",
            "                   all          6         25      0.612       0.84      0.789      0.425\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    210/332     0.359G     0.0331    0.03471          0         69        256: 100% 3/3 [00:00<00:00,  8.58it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.38it/s]\n",
            "                   all          6         25      0.635       0.92      0.789      0.415\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    211/332     0.359G    0.03417    0.03748          0        103        256: 100% 3/3 [00:00<00:00, 10.01it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.33it/s]\n",
            "                   all          6         25      0.647       0.92      0.779      0.429\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    212/332     0.359G    0.03452    0.03488          0         88        256: 100% 3/3 [00:00<00:00, 10.32it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.89it/s]\n",
            "                   all          6         25      0.669       0.92      0.791      0.423\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    213/332     0.359G    0.03466    0.03618          0        103        256: 100% 3/3 [00:00<00:00, 11.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.95it/s]\n",
            "                   all          6         25      0.689       0.92      0.801      0.453\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    214/332     0.359G    0.03367    0.03604          0         99        256: 100% 3/3 [00:00<00:00, 10.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.28it/s]\n",
            "                   all          6         25       0.69       0.89      0.805      0.457\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    215/332     0.359G    0.03575    0.03789          0         74        256: 100% 3/3 [00:00<00:00,  9.57it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.20it/s]\n",
            "                   all          6         25      0.742      0.808      0.826      0.466\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    216/332     0.359G    0.03234    0.03418          0         71        256: 100% 3/3 [00:00<00:00,  8.24it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.51it/s]\n",
            "                   all          6         25      0.697      0.828      0.809      0.471\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    217/332     0.359G    0.03368    0.03425          0         77        256: 100% 3/3 [00:00<00:00,  9.11it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.04it/s]\n",
            "                   all          6         25      0.678       0.84      0.816      0.456\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    218/332     0.359G    0.03515    0.03712          0        101        256: 100% 3/3 [00:00<00:00,  9.14it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.74it/s]\n",
            "                   all          6         25      0.674       0.92      0.803       0.46\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    219/332     0.359G      0.034    0.03683          0        115        256: 100% 3/3 [00:00<00:00,  6.83it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.31it/s]\n",
            "                   all          6         25      0.674       0.92      0.796      0.452\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    220/332     0.359G    0.03408    0.03285          0         71        256: 100% 3/3 [00:00<00:00,  5.91it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.77it/s]\n",
            "                   all          6         25      0.668       0.92      0.802      0.455\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    221/332     0.359G    0.03415     0.0358          0         71        256: 100% 3/3 [00:00<00:00,  5.88it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.24it/s]\n",
            "                   all          6         25      0.665       0.92       0.79      0.422\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    222/332     0.359G    0.03567    0.03913          0        104        256: 100% 3/3 [00:00<00:00, 11.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.00it/s]\n",
            "                   all          6         25      0.671       0.92      0.795      0.418\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    223/332     0.359G     0.0352     0.0396          0        100        256: 100% 3/3 [00:00<00:00,  8.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.79it/s]\n",
            "                   all          6         25      0.681       0.96        0.8      0.429\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    224/332     0.359G    0.03609    0.03569          0         83        256: 100% 3/3 [00:00<00:00, 13.10it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.00it/s]\n",
            "                   all          6         25      0.704      0.953      0.811      0.436\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    225/332     0.359G    0.03368    0.03622          0        102        256: 100% 3/3 [00:00<00:00,  9.00it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.91it/s]\n",
            "                   all          6         25       0.74       0.76      0.794      0.435\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    226/332     0.359G    0.03392    0.03785          0        114        256: 100% 3/3 [00:00<00:00,  8.61it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.16it/s]\n",
            "                   all          6         25        0.7       0.96      0.823      0.433\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    227/332     0.359G    0.03502    0.04019          0        117        256: 100% 3/3 [00:00<00:00,  8.82it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.81it/s]\n",
            "                   all          6         25      0.764      0.778        0.8      0.427\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    228/332     0.359G     0.0339    0.03354          0         73        256: 100% 3/3 [00:00<00:00, 13.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.36it/s]\n",
            "                   all          6         25      0.796       0.78      0.794      0.431\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    229/332     0.359G    0.03277    0.03407          0         98        256: 100% 3/3 [00:00<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.41it/s]\n",
            "                   all          6         25      0.666          1      0.825      0.406\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    230/332     0.359G    0.03449    0.03589          0         79        256: 100% 3/3 [00:00<00:00,  9.73it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.84it/s]\n",
            "                   all          6         25      0.708      0.871      0.785      0.418\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    231/332     0.359G    0.03331    0.03711          0         91        256: 100% 3/3 [00:00<00:00, 10.83it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.69it/s]\n",
            "                   all          6         25      0.628       0.92      0.786      0.417\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    232/332     0.359G    0.03217    0.03636          0         92        256: 100% 3/3 [00:00<00:00, 11.04it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.33it/s]\n",
            "                   all          6         25      0.652       0.96      0.812      0.448\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    233/332     0.359G    0.03247    0.03522          0        103        256: 100% 3/3 [00:00<00:00, 11.58it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.13it/s]\n",
            "                   all          6         25      0.645       0.96       0.81      0.436\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    234/332     0.359G    0.03306    0.04088          0        141        256: 100% 3/3 [00:00<00:00, 13.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.09it/s]\n",
            "                   all          6         25      0.645      0.946      0.815      0.444\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    235/332     0.359G    0.03427    0.03617          0         94        256: 100% 3/3 [00:00<00:00,  5.64it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.08it/s]\n",
            "                   all          6         25      0.693       0.84      0.817      0.441\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    236/332     0.359G    0.03292    0.03858          0        107        256: 100% 3/3 [00:00<00:00,  6.16it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.83it/s]\n",
            "                   all          6         25      0.651       0.88      0.805      0.444\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    237/332     0.359G    0.03491     0.0358          0         82        256: 100% 3/3 [00:00<00:00,  8.44it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.39it/s]\n",
            "                   all          6         25      0.663       0.92      0.797      0.421\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    238/332     0.359G    0.03362    0.03339          0         80        256: 100% 3/3 [00:00<00:00, 11.26it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.35it/s]\n",
            "                   all          6         25      0.675      0.916      0.792       0.41\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    239/332     0.359G    0.03403    0.03503          0        102        256: 100% 3/3 [00:00<00:00,  9.82it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.93it/s]\n",
            "                   all          6         25      0.628       0.92      0.782      0.417\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    240/332     0.359G    0.03399    0.03133          0         77        256: 100% 3/3 [00:00<00:00, 11.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.20it/s]\n",
            "                   all          6         25      0.666       0.96      0.797      0.429\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    241/332     0.359G    0.03315    0.03734          0         99        256: 100% 3/3 [00:00<00:00,  8.96it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.90it/s]\n",
            "                   all          6         25      0.645       0.96       0.79      0.425\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    242/332     0.359G    0.03484    0.03659          0         99        256: 100% 3/3 [00:00<00:00, 11.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.43it/s]\n",
            "                   all          6         25      0.691          1      0.825      0.423\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    243/332     0.359G    0.03371    0.03467          0         95        256: 100% 3/3 [00:00<00:00,  9.14it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.06it/s]\n",
            "                   all          6         25      0.677       0.96       0.83      0.435\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    244/332     0.359G    0.03321    0.03587          0         97        256: 100% 3/3 [00:00<00:00,  8.47it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.25it/s]\n",
            "                   all          6         25      0.733       0.88      0.808      0.423\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    245/332     0.359G     0.0332    0.03842          0         94        256: 100% 3/3 [00:00<00:00, 10.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.18it/s]\n",
            "                   all          6         25      0.726      0.848      0.807      0.419\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    246/332     0.359G    0.03544    0.03864          0        106        256: 100% 3/3 [00:00<00:00,  9.08it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.94it/s]\n",
            "                   all          6         25      0.681       0.84      0.804      0.441\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    247/332     0.359G    0.03379    0.03539          0        118        256: 100% 3/3 [00:00<00:00, 12.74it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.07it/s]\n",
            "                   all          6         25       0.66      0.933      0.814      0.457\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    248/332     0.359G    0.03292    0.03457          0         86        256: 100% 3/3 [00:00<00:00,  8.07it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.90it/s]\n",
            "                   all          6         25      0.674      0.909      0.812      0.461\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    249/332     0.359G    0.03309    0.03572          0         85        256: 100% 3/3 [00:00<00:00,  7.74it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.13it/s]\n",
            "                   all          6         25      0.691      0.986      0.849       0.45\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    250/332     0.359G     0.0337    0.03727          0         98        256: 100% 3/3 [00:00<00:00,  9.29it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.97it/s]\n",
            "                   all          6         25      0.763        0.8      0.829      0.457\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    251/332     0.359G    0.03242    0.03673          0        108        256: 100% 3/3 [00:00<00:00,  5.20it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.48it/s]\n",
            "                   all          6         25       0.66       0.92      0.836      0.479\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    252/332     0.359G    0.03372    0.03619          0         84        256: 100% 3/3 [00:00<00:00,  6.73it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.66it/s]\n",
            "                   all          6         25      0.751        0.8      0.829       0.46\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    253/332     0.359G    0.03304    0.03653          0         93        256: 100% 3/3 [00:00<00:00,  6.45it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.71it/s]\n",
            "                   all          6         25      0.742        0.8      0.813      0.471\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    254/332     0.359G    0.03326    0.03457          0        107        256: 100% 3/3 [00:00<00:00,  9.15it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.10it/s]\n",
            "                   all          6         25      0.711        0.8      0.817       0.46\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    255/332     0.359G    0.03216    0.03646          0        100        256: 100% 3/3 [00:00<00:00,  9.71it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.74it/s]\n",
            "                   all          6         25      0.732       0.76      0.816      0.465\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    256/332     0.359G    0.03425    0.03614          0         91        256: 100% 3/3 [00:00<00:00,  8.65it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.54it/s]\n",
            "                   all          6         25      0.737       0.76      0.814      0.443\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    257/332     0.359G     0.0347    0.03735          0         96        256: 100% 3/3 [00:00<00:00,  8.22it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.67it/s]\n",
            "                   all          6         25      0.738        0.8      0.809      0.442\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    258/332     0.359G     0.0339    0.03772          0        104        256: 100% 3/3 [00:00<00:00, 12.15it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.32it/s]\n",
            "                   all          6         25      0.737        0.8      0.805       0.44\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    259/332     0.359G    0.03393    0.03486          0         86        256: 100% 3/3 [00:00<00:00,  8.80it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.26it/s]\n",
            "                   all          6         25      0.739        0.8      0.803      0.431\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    260/332     0.359G    0.03263    0.03577          0         70        256: 100% 3/3 [00:00<00:00,  9.00it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.74it/s]\n",
            "                   all          6         25      0.735        0.8      0.798      0.429\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    261/332     0.359G    0.03155    0.03512          0         99        256: 100% 3/3 [00:00<00:00,  9.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.40it/s]\n",
            "                   all          6         25      0.719        0.8      0.778      0.419\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    262/332     0.359G    0.03375    0.03822          0        108        256: 100% 3/3 [00:00<00:00,  9.35it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.83it/s]\n",
            "                   all          6         25      0.685      0.869      0.791      0.416\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    263/332     0.359G    0.03342     0.0358          0         86        256: 100% 3/3 [00:00<00:00,  8.32it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.46it/s]\n",
            "                   all          6         25      0.645       0.88      0.789      0.443\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    264/332     0.359G    0.03147    0.03592          0         92        256: 100% 3/3 [00:00<00:00, 10.24it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.86it/s]\n",
            "                   all          6         25      0.662       0.88      0.788      0.431\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    265/332     0.359G    0.03399    0.03654          0        108        256: 100% 3/3 [00:00<00:00,  8.13it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.43it/s]\n",
            "                   all          6         25      0.671        0.9      0.781      0.415\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    266/332     0.359G    0.03115    0.03302          0         89        256: 100% 3/3 [00:00<00:00,  7.81it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.42it/s]\n",
            "                   all          6         25      0.662       0.88      0.797      0.412\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    267/332     0.359G    0.03063    0.03461          0         75        256: 100% 3/3 [00:00<00:00,  6.71it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.50it/s]\n",
            "                   all          6         25      0.674      0.911      0.791      0.419\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    268/332     0.359G    0.03358    0.03911          0        111        256: 100% 3/3 [00:00<00:00,  7.33it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.06it/s]\n",
            "                   all          6         25      0.673      0.905      0.801       0.41\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    269/332     0.359G    0.03228    0.03639          0        101        256: 100% 3/3 [00:00<00:00,  6.37it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.80it/s]\n",
            "                   all          6         25      0.675      0.913      0.803      0.414\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    270/332     0.359G    0.03132    0.03514          0        102        256: 100% 3/3 [00:00<00:00, 13.52it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.93it/s]\n",
            "                   all          6         25      0.639       0.92      0.804       0.42\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    271/332     0.359G    0.03278    0.03426          0         92        256: 100% 3/3 [00:00<00:00,  7.28it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.32it/s]\n",
            "                   all          6         25      0.663      0.944      0.798      0.435\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    272/332     0.359G    0.03166    0.03431          0         86        256: 100% 3/3 [00:00<00:00,  7.85it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.39it/s]\n",
            "                   all          6         25      0.682      0.943      0.796      0.436\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    273/332     0.359G    0.03045    0.03411          0         76        256: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.41it/s]\n",
            "                   all          6         25      0.696      0.915      0.796      0.436\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    274/332     0.359G    0.03208    0.03389          0         73        256: 100% 3/3 [00:00<00:00,  5.83it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.19it/s]\n",
            "                   all          6         25      0.689       0.92      0.797      0.434\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    275/332     0.359G    0.03306    0.03625          0        111        256: 100% 3/3 [00:00<00:00,  4.94it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.01it/s]\n",
            "                   all          6         25      0.681      0.939      0.799      0.417\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    276/332     0.359G    0.03125    0.03648          0        117        256: 100% 3/3 [00:00<00:00,  9.12it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.50it/s]\n",
            "                   all          6         25      0.698       0.96      0.817       0.41\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    277/332     0.359G    0.03177    0.03706          0        105        256: 100% 3/3 [00:00<00:00, 10.52it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.51it/s]\n",
            "                   all          6         25      0.697       0.96      0.815      0.421\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    278/332     0.359G    0.03227    0.03645          0        117        256: 100% 3/3 [00:00<00:00,  9.65it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.63it/s]\n",
            "                   all          6         25      0.678       0.96      0.817      0.419\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    279/332     0.359G    0.03204    0.03515          0         86        256: 100% 3/3 [00:00<00:00,  7.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.89it/s]\n",
            "                   all          6         25      0.693      0.995       0.83      0.435\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    280/332     0.359G     0.0319    0.03359          0         80        256: 100% 3/3 [00:00<00:00,  7.48it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.85it/s]\n",
            "                   all          6         25      0.693      0.991      0.831      0.432\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    281/332     0.359G    0.03592    0.03536          0         78        256: 100% 3/3 [00:00<00:00,  6.10it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.29it/s]\n",
            "                   all          6         25      0.689          1      0.829      0.431\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    282/332     0.359G    0.03134    0.03559          0        102        256: 100% 3/3 [00:00<00:00,  5.16it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.80it/s]\n",
            "                   all          6         25      0.683          1      0.834      0.442\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    283/332     0.359G    0.03281    0.03617          0        100        256: 100% 3/3 [00:00<00:00,  8.31it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.73it/s]\n",
            "                   all          6         25      0.684          1      0.839      0.452\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    284/332     0.359G    0.03302    0.04282          0        122        256: 100% 3/3 [00:00<00:00,  7.01it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.55it/s]\n",
            "                   all          6         25      0.685          1      0.839      0.438\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    285/332     0.359G     0.0327    0.03678          0         93        256: 100% 3/3 [00:00<00:00, 10.14it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.68it/s]\n",
            "                   all          6         25      0.682          1      0.834      0.435\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    286/332     0.359G     0.0321    0.03474          0         93        256: 100% 3/3 [00:00<00:00, 11.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.67it/s]\n",
            "                   all          6         25      0.678          1      0.832      0.433\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    287/332     0.359G    0.03269    0.03509          0        104        256: 100% 3/3 [00:00<00:00,  9.42it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.85it/s]\n",
            "                   all          6         25      0.708          1      0.841      0.444\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    288/332     0.359G    0.03143    0.03334          0         77        256: 100% 3/3 [00:00<00:00, 10.75it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.85it/s]\n",
            "                   all          6         25      0.709          1      0.839      0.445\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    289/332     0.359G    0.03174    0.03766          0         92        256: 100% 3/3 [00:00<00:00,  9.42it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.67it/s]\n",
            "                   all          6         25      0.707          1      0.833      0.442\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    290/332     0.359G    0.03225    0.03523          0         84        256: 100% 3/3 [00:00<00:00,  9.70it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.89it/s]\n",
            "                   all          6         25      0.705          1      0.831       0.44\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    291/332     0.359G    0.03211    0.03705          0         98        256: 100% 3/3 [00:00<00:00,  8.79it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.07it/s]\n",
            "                   all          6         25      0.659      0.928      0.801      0.437\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    292/332     0.359G    0.03251    0.03364          0         92        256: 100% 3/3 [00:00<00:00,  8.97it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.87it/s]\n",
            "                   all          6         25      0.661       0.96        0.8      0.439\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    293/332     0.359G    0.03298    0.03634          0         86        256: 100% 3/3 [00:00<00:00, 11.36it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.52it/s]\n",
            "                   all          6         25      0.683       0.96      0.811       0.44\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    294/332     0.359G    0.03158    0.03408          0         86        256: 100% 3/3 [00:00<00:00,  8.41it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.31it/s]\n",
            "                   all          6         25       0.67       0.96      0.809      0.444\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    295/332     0.359G    0.03326    0.03629          0         69        256: 100% 3/3 [00:00<00:00,  8.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.10it/s]\n",
            "                   all          6         25      0.689          1      0.836      0.446\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    296/332     0.359G    0.03162    0.03451          0         86        256: 100% 3/3 [00:00<00:00,  7.35it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.73it/s]\n",
            "                   all          6         25      0.659       0.96      0.804      0.439\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    297/332     0.359G    0.03331    0.03571          0         89        256: 100% 3/3 [00:00<00:00,  4.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.25it/s]\n",
            "                   all          6         25      0.661       0.96      0.809       0.45\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    298/332     0.359G    0.03027    0.03381          0         87        256: 100% 3/3 [00:00<00:00,  6.62it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.08it/s]\n",
            "                   all          6         25       0.66       0.96      0.809      0.452\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    299/332     0.359G    0.03208    0.03414          0         89        256: 100% 3/3 [00:00<00:00,  8.10it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.65it/s]\n",
            "                   all          6         25      0.661       0.96      0.808       0.44\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    300/332     0.359G    0.03193    0.03643          0         92        256: 100% 3/3 [00:00<00:00, 10.10it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.29it/s]\n",
            "                   all          6         25      0.661       0.96      0.807      0.449\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    301/332     0.359G      0.032     0.0383          0        107        256: 100% 3/3 [00:00<00:00,  9.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.05it/s]\n",
            "                   all          6         25      0.663       0.96      0.804      0.437\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    302/332     0.359G    0.03248    0.03515          0        101        256: 100% 3/3 [00:00<00:00,  9.39it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.31it/s]\n",
            "                   all          6         25      0.663       0.96      0.804      0.442\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    303/332     0.359G    0.03386     0.0366          0         90        256: 100% 3/3 [00:00<00:00, 10.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.45it/s]\n",
            "                   all          6         25      0.669       0.96      0.799      0.435\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    304/332     0.359G    0.03249    0.03603          0         99        256: 100% 3/3 [00:00<00:00,  9.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.67it/s]\n",
            "                   all          6         25      0.681       0.96      0.798       0.45\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    305/332     0.359G    0.03135    0.03394          0         96        256: 100% 3/3 [00:00<00:00,  9.18it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.61it/s]\n",
            "                   all          6         25      0.645      0.944      0.802      0.441\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    306/332     0.359G    0.03086    0.03389          0         89        256: 100% 3/3 [00:00<00:00,  9.84it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.10it/s]\n",
            "                   all          6         25      0.685       0.84      0.805       0.44\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    307/332     0.359G    0.03243    0.03456          0         88        256: 100% 3/3 [00:00<00:00,  8.81it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.20it/s]\n",
            "                   all          6         25      0.661       0.84       0.81       0.45\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    308/332     0.359G    0.03075    0.03588          0         96        256: 100% 3/3 [00:00<00:00,  8.92it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.10it/s]\n",
            "                   all          6         25       0.65       0.92      0.807       0.46\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    309/332     0.359G    0.03108     0.0348          0         76        256: 100% 3/3 [00:00<00:00,  7.98it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.28it/s]\n",
            "                   all          6         25      0.664       0.92      0.803      0.465\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    310/332     0.359G    0.03175      0.035          0        102        256: 100% 3/3 [00:00<00:00,  9.26it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.72it/s]\n",
            "                   all          6         25       0.68       0.92      0.809      0.463\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    311/332     0.359G    0.03319    0.03518          0         74        256: 100% 3/3 [00:00<00:00,  7.83it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.52it/s]\n",
            "                   all          6         25      0.661      0.935      0.813      0.447\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    312/332     0.359G    0.03304    0.03815          0        109        256: 100% 3/3 [00:00<00:00,  5.89it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.87it/s]\n",
            "                   all          6         25      0.689          1      0.838      0.444\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    313/332     0.359G    0.03151    0.03479          0         97        256: 100% 3/3 [00:00<00:00,  5.76it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.98it/s]\n",
            "                   all          6         25      0.729          1      0.845      0.447\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    314/332     0.359G    0.03018    0.03079          0         75        256: 100% 3/3 [00:00<00:00,  6.71it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  7.69it/s]\n",
            "                   all          6         25      0.745          1       0.84      0.451\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    315/332     0.359G    0.03231    0.03427          0         83        256: 100% 3/3 [00:00<00:00,  7.71it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.68it/s]\n",
            "                   all          6         25      0.715      0.904      0.808      0.453\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    316/332     0.359G    0.03054    0.03386          0         93        256: 100% 3/3 [00:00<00:00,  9.11it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.36it/s]\n",
            "                   all          6         25      0.715      0.901      0.809      0.448\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    317/332     0.359G    0.03203    0.03479          0         91        256: 100% 3/3 [00:00<00:00, 10.31it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.45it/s]\n",
            "                   all          6         25      0.717      0.912      0.805      0.449\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    318/332     0.359G    0.03224    0.03318          0         75        256: 100% 3/3 [00:00<00:00, 10.09it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.50it/s]\n",
            "                   all          6         25      0.731       0.96      0.838      0.453\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    319/332     0.359G    0.03127    0.03414          0         86        256: 100% 3/3 [00:00<00:00,  7.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.42it/s]\n",
            "                   all          6         25       0.71      0.978      0.837      0.439\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    320/332     0.359G    0.03193    0.03523          0         86        256: 100% 3/3 [00:00<00:00,  9.92it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.27it/s]\n",
            "                   all          6         25      0.711      0.982      0.837      0.444\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    321/332     0.359G     0.0321    0.03461          0         99        256: 100% 3/3 [00:00<00:00,  7.99it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.24it/s]\n",
            "                   all          6         25      0.709      0.975      0.837      0.445\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    322/332     0.359G    0.03157    0.03364          0         91        256: 100% 3/3 [00:00<00:00,  9.08it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.75it/s]\n",
            "                   all          6         25      0.708      0.968      0.837      0.442\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    323/332     0.359G    0.03193    0.03589          0        117        256: 100% 3/3 [00:00<00:00,  8.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 10.94it/s]\n",
            "                   all          6         25       0.71      0.977      0.835      0.447\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    324/332     0.359G    0.03253    0.03345          0         67        256: 100% 3/3 [00:00<00:00, 10.58it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.67it/s]\n",
            "                   all          6         25       0.71       0.98      0.831      0.443\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    325/332     0.359G     0.0314    0.03468          0         97        256: 100% 3/3 [00:00<00:00,  9.80it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.81it/s]\n",
            "                   all          6         25      0.711      0.987      0.828      0.448\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    326/332     0.359G    0.03177      0.033          0         84        256: 100% 3/3 [00:00<00:00, 10.43it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.87it/s]\n",
            "                   all          6         25      0.712      0.989      0.829      0.445\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    327/332     0.359G    0.03122    0.03472          0        100        256: 100% 3/3 [00:00<00:00,  5.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  8.47it/s]\n",
            "                   all          6         25      0.714      0.999      0.831      0.455\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    328/332     0.359G    0.03201    0.03327          0         88        256: 100% 3/3 [00:00<00:00,  6.82it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  6.63it/s]\n",
            "                   all          6         25      0.713          1      0.831      0.456\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    329/332     0.359G    0.03073    0.03103          0         78        256: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  5.74it/s]\n",
            "                   all          6         25      0.707          1      0.831      0.449\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    330/332     0.359G    0.03152    0.03577          0         76        256: 100% 3/3 [00:00<00:00,  7.09it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00,  9.37it/s]\n",
            "                   all          6         25      0.709          1      0.831      0.449\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    331/332     0.359G    0.03026    0.03429          0         80        256: 100% 3/3 [00:00<00:00,  9.12it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.48it/s]\n",
            "                   all          6         25      0.708          1      0.832      0.454\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    332/332     0.359G    0.03378    0.03546          0         96        256: 100% 3/3 [00:00<00:00, 10.33it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 11.52it/s]\n",
            "                   all          6         25      0.706          1      0.829      0.453\n",
            "\n",
            "333 epochs completed in 0.086 hours.\n",
            "Optimizer stripped from runs/train/exp/weights/last.pt, 3.7MB\n",
            "Optimizer stripped from runs/train/exp/weights/best.pt, 3.7MB\n",
            "\n",
            "Validating runs/train/exp/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 157 layers, 1760518 parameters, 0 gradients, 4.1 GFLOPs\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:00<00:00, 12.18it/s]\n",
            "                   all          6         25      0.659      0.928       0.84      0.482\n",
            "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AcIRLQOlA14A"
      },
      "source": [
        "# Evaluate Custom YOLOv5 Detector Performance\n",
        "Training losses and performance metrics are saved to Tensorboard and also to a logfile.\n",
        "\n",
        "If you are new to these metrics, the one you want to focus on is `mAP_0.5` - learn more about mean average precision [here](https://blog.roboflow.com/mean-average-precision/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtmS7_TXFsT3"
      },
      "source": [
        "#Run Inference  With Trained Weights\n",
        "Run inference with a pretrained checkpoint on contents of `test/images` folder downloaded from Roboflow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWjjiBcic3Vz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d40a3b40-4d43-40b5-e539-6fa9bb3e1e4c"
      },
      "source": [
        "!python detect.py --weights runs/train/exp/weights/best.pt --img {IMG_SIZE} --conf 0.5 --source {dataset.location}/test/images --max-det 20 --line-thickness 1"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['runs/train/exp/weights/best.pt'], source=/content/yolov5/Learned-Frontier-Detection-4/test/images, data=data/coco128.yaml, imgsz=[256, 256], conf_thres=0.5, iou_thres=0.45, max_det=20, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=1, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
            "YOLOv5 🚀 v7.0-140-g1db9533 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 157 layers, 1760518 parameters, 0 gradients, 4.1 GFLOPs\n",
            "image 1/6 /content/yolov5/Learned-Frontier-Detection-4/test/images/16_506_957_0-030000_png.rf.3b4b1d285ff80bb3ba15cd5b6dc3b8b3.jpg: 256x256 4 frontiers, 5.7ms\n",
            "image 2/6 /content/yolov5/Learned-Frontier-Detection-4/test/images/1_503_821_0-030000_png.rf.ca73635a2cd9b1e5dec28dd67ce25017.jpg: 256x256 4 frontiers, 5.4ms\n",
            "image 3/6 /content/yolov5/Learned-Frontier-Detection-4/test/images/2_503_860_0-030000_png.rf.5b28662cd3c25c74f583ab3745f25073.jpg: 256x256 4 frontiers, 5.1ms\n",
            "image 4/6 /content/yolov5/Learned-Frontier-Detection-4/test/images/6_504_1005_0-030000_png.rf.636de66d43575f5c21cd073f9389a36e.jpg: 256x256 4 frontiers, 8.5ms\n",
            "image 5/6 /content/yolov5/Learned-Frontier-Detection-4/test/images/8_504_1004_0-030000_png.rf.b6d0f978b60471568b0eefba77fcf983.jpg: 256x256 4 frontiers, 7.1ms\n",
            "image 6/6 /content/yolov5/Learned-Frontier-Detection-4/test/images/9_504_1005_0-030000_png.rf.c448732b7ce7e55e7ea5b445fce0ea84.jpg: 256x256 5 frontiers, 5.1ms\n",
            "Speed: 0.2ms pre-process, 6.2ms inference, 15.0ms NMS per image at shape (1, 3, 256, 256)\n",
            "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZbUn4_b9GCKO",
        "outputId": "d96b8614-75fa-471b-83b0-eff3a3c5f2a4"
      },
      "source": [
        "#display inference on ALL test images\n",
        "\n",
        "import glob\n",
        "from IPython.display import Image, display\n",
        "\n",
        "for imageName in glob.glob('/content/yolov5/runs/detect/exp/*.jpg'): #assuming JPG\n",
        "    display(Image(filename=imageName))\n",
        "    print(\"\\n\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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/Niiiiuw+eCiiigAooooAKKKKACoNT/5B83/XM1PUGp/8g+b/AK5muXG/7nU/wy/JnucM/wDJSYL/AK/U/wD0uJ+4P7Jv/Jqnwy/7J9o3/pDDXoVee/sm/J+yr8Mvb4faN/6QQ16FXUeGFFFFABRRRQAV5b8W/j7YfCL40eAfAniaCFNJ8atfWUWotwLa+j8n7NF0/wCWvmSRj3Ar1KvkD/gsn4Zmuf2ddB8YWHmx3GjeL4SZ4usccsUoz/39EVAH1/RXzv8A8E7v2p5f2sPgNNrep6bfJq3g7WR4e165vNnl393HYWl0LmIjnBjuos/6v955gxxX0RQAUUUUAFFFFABXhn7WP7NugftTXmifC7xZ4+8R6Bpv9n39xfTeGbu1imufLmscRSfaYZsx5IkxjrFGea9zrwP9uT4l/Gn4SeErPxn8BPhNrXi/xFdW9zpNlb6HpxuDZSXMtqftUuCMRRxxSnvmTywRQB8efEX9hb9iz4f/ALTfhT9maH47/FC41PWbzytXuo9Q0by9LMkR+zR/8gv/AFksvlf56fphpVo+l6Vb6a97cXRt4Y4zc3LZlkIH+sfp6fzr8Yvhv+z7/wAFAde+Jen/ABN8YfAnVtF1X+14tSu9Q1u0165lil821l82TytLl82X97J/4Cy/9MvN/aLTbz+0rKG+MEkQli3CKePZInsR2oA80/aw+E+nfGv4QzeBtb8Z3ugadNfQ/wBq6jp2oRW8sVtgxSbHmiljH+s7pz7V+QHxs8N/B3wt8S7zQ/gR4x8Ua34fsJpbaHUfEM1rL9v/AOmsXlWsX7qv1p/b/wBH+Iuu/sjeMtI+FOhXWpa7c2dtFa2FnBJLLLF9qi80RpHyZPK8zGPSvyF179n39rPSLGO38ffsweNfDdvqvl20OrXenS+Va3VzFF5cUsv/AF1l8r/tlQB+w/7C+nafp/7I3w/TTXnlT/hG4JPOu4/3vmPzJn8Sa8J/4LY2tvffAHwmlxO6KPGkbGWPqGS2uD/MV73+xR4d8TeFP2WfBPhnxrokunarY6QY77T7qHy5IT5knBj7cYrwf/gtH4V8a+M/gX4Q8NfDrw/e6trFx41iNjpumQ+Zc3DpbXEjqqdzgE4+tceO/wB3/wC3o/8ApSPoeF/+Rt/3Dr/+mKh+alFRwzTz+ZBPY3NtJFN5c0V3D5UtSV2HzwUV0Xgf4I/Gv4q2lxcfCL4Uar4nitpoorv+y4sfZfNl8s/oD+VYusaD4q8K6zeeHPHHg3UvD+qWE3l3mk6tD5UsX7rzf/atAFeiiigAooooAKg1P/kHzf8AXM1PUGp/8g+b/rma5cb/ALnU/wAMvyZ7nDP/ACUmC/6/U/8A0uJ+437MdrJZ/s0/DyznP7yLwNo8cg+lpED/AFrva8A/4Jn6J4+0X9hn4eN8S/GsWv6lqekS6rBqEF3LcRnT72aW6sbYSS8nyrWa2h/7Ze/Pv9dR4YUUUUAFFFFABXjf7dXwD8XftN/sweJvg14G1a0sdZ1iK2Gn6jdyeXHayR3UUnmD93J/AH7H075HslFAHz3+wb+xuf2MNN+JPg3StSa90jxP8RF1vQri6vDPdG1/sXS7D/Sf3UUfm+bYy8RjHleVzmvoSiigAooooAKKKKACiivMP2m/jzrvwC+HEnijwj8IvE/jTV7g+Xp2k+GtAu77MmOsptopTFH7kUAd9ca3pFtqNvpk+owJd3fmfZLWSQeZL5f+s2DqcVo1+QLfFX9uG1/aEsP2q/HXwM+Kk9/o+pxzGC3+G2siKK2J8ua1iiFr/qzGfL/Gv1v0u+OradBqcVtcQ/aIkcRXcPlyx5/vp2PtQBfr83f+Ctf7Ul5q3xa0f4MeB/EAFp4Smj1HWPJ/6Cmf3X/fqL/0bX3F+0h8Wbj4IfBfWviVpfh6/wBWv7aOG30nS9O06W5lur65mjtraIRRAyHMssQOMcH6V+O/jD4QftQanf6p448Yfs9fEwyXU0t7qWoXfw91T/rrLLL/AKLQB+2+ha3aeJNEtNbsJBJBfWsVxF/1zkGR+leBf8FBPFx8E6b4I8bx3Qg/snXNVuY5f7rxaHqRz+YFd9+x1rsviP8AZb+H2q31leW1x/wiFjFLDfWUtvKDFCIzmOX94PXn1rw7/gr/AG2v6h8H/B+k+F9Fv9Q1K+8VvZ2djptk9xcTNLYXUQCxR8sTu6DtXHjv93/7ej/6Uj6Hhf8A5G3/AHDr/wDpiofmJRVzxJ4b8Y+CtZj8OeP/AAB4k0DUJYfMhtPEPhu6spfK/wC2sX/TWtz4KfD6f4s/F7wv8M4f+Y7r1rZTf9MopZf3v/kKuw+eP0q/Yb+Glv8AsXfsV3/xD+It3byXN/aSeJ9SWD90Y4vssXlWvP8Ay04/7+ymvzB8eeMNc+IXjLVPHHiObzdQ1nUpb28/66yy+bX6N/8ABYz4zJ8P/gRofwV0N5IJPGF5KZfKH7sWFj5Xmx+372W2H51+aFABRRRQAUUUUAFQan/yD5v+uZqetPQfAHizx7b6onhXSprpNP0ifUdQkVci3t40LGRpO6HGB7muXG/7nU/wy/JnucM/8lJgv+v1P/0uJ+w//BPL/kwj4I/9ki8N/wDprtq9irx3/gnl/wAmEfBH/skXhv8A9NdtXsVdR4YUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVzmpeONF8NaZrvibxXex6VpmhzD7RqF5IPKMQiileXPYDzDH9Y66OvirVv2dPgT4P0n4kfs7ftQ/tX6fp+l+J/ilqXj7SrS38Qpo11FBqM0119lk+03M3nRxS+Z+8/dRHyo8RR4oA534I/wDBT+78fftt3eh65eyxeAPERh0vw1ayQ+UbGUcxXUv/AF15/wC/0f8Azyr73r4O8A/8E5P+CevxD8Xt4d+HPxY8V6xLawzSR3mk+K7a4t5DF9l8wCSGLny/tNt3Hb/nnx9xaXZf2dpsOli5lmFvFHH5tzL5ksnH8fvQB59+1VrtvoHw40e8mmiijl+I/hOKSSWbywPM1+wFfC//AAUa/wCChVz8Zb68+BfwZv2j8K205j1fVYz/AMheWPnEf/Tr/wCje1fX3/BQ/wAB6X8Uf2dLf4a61eXltZa78RfCVley2E/ly+VJ4gsN+w/57153/wAOVv2S/wDoYvHX/g/i/wDjNAHqP7CetCD9ivwRr2v6m5S30EvcXU3eOOWTk/gK8K/4KK/tTWWsfs3fDn9pj9nfxW00beOJF0bV0tyBlYLy1eRI5PffjPXAr6g0L4SeHPhL+z1J8HvBM1ydP0zw5c2VlLqM/my48qTmSTvyetfmX4sOnyf8EXfgzc6hO/kweIr6SKaN44zvj/tGRMEcD9761x47/d/+3o/+lI+h4X/5G3/cOv8A+mKh4L4k8SeI/GGs3HiPxVrlzqWoXU3mXl3dzebLLX0v/wAEiPhwPGn7WUfiy4iD23hbR7q9Of8AnrL/AKLH/wCjJf8Av1XgHwx+EHxM+Muunw78LPBGpa3ef8t4tPh8zyv+usv/ACyr9Mv+CaH7H3jj9lzwfr2ufE+Kxj17xJNbYtrSYy/ZraJciKST+KTzJZa7D548b/4Lo/8AIe+FH/Xn4j/npdfBtfof/wAFftb+Amj+Ivh8vxc+E934x1GTR9Y/si2i8bahpEdifO0/MsgsZYzKP/jVfBGg/DfXPHniO8vvhl8Ob77P/wAtrTT7y/ubWwi/663csvlf9dZZaAMetr4o/Dbxr8JPG+m+FfGsEccmr/D/AEvxNFa/8vNrHc3WqRfvP+2VjFL/ANta+nv+Cen7CmifGLx1F8Q/GvjPR9R0jwzeQyXeiWDyXHmSZkkjiklwYs/u/wB4IpJQTgcZrk/+CwTWjf8ABRS8hu9IjeSX4HeF8SGeaOWH/ic+JP8AnnII/wA6APnOio4YfJjjgg8z91/z2m82u3+BXwK+In7RnxEtPh38PNL+0Xcv7y8u5f8AVWEX/PWT/plQAfAn4FfEX9of4iWfw7+Hel/abuX95Ndzf6q1i/56yf8ATKv0l8U/sqfDz9lb9gv4heEfCUX2i/uPB19Lq+tSx4mvZfJPOf4U9E7V6f8AsufsqfDv9ln4dp4O8GQ/aLyUebq2sTQ/vb2Xjk46J1xH0FT/ALaX/JpPxH/7E6//APRLVy43/c6n+GX5M9zhn/kpMF/1+p/+lxOa/wCCdfgPx98M/wBjf4f+BviFrlpqNzp+kMunTWXEcel+bIdNhH/XKx+zRd+YzzJ/rD7nXG/s7/8AJv8A4G/7E/S//SSKuyrqPDCiiigAooooAKKKKACiiigAooooAKKKKACuG+NPj/xz8NPCD658Pvghr3xA1NpPLg0bRb6ytSOOskl3NEBH15HmH2ruaKAPgvxf+0F/wWJ8RfEDTNc0L9jDUNE0Sx1GOWfw9aaxoshv4v8AnlLcy3//AKK8qvuTT7me90mC9uLGWzeSKN5bWTBkhz/yz/d+laFFAHiv7b2kfEO6+BCaj8LPhbqvjfWdI8aeGNYg8NaRe2Ntc30VjrVhdTCOW+mihB8qKTmSQfWvlX4p/th/8FwPG08mn/Cz/gmDrfgq0lSNIbq88T+F9Suoz5kX7zzP7V8o8ebx5XvX6KUUAeWfs7Xfx0139mnwxdfHGxm0X4hXXhmE6rDdG2kktL7ygCJRDJLCZM4Mnlkx5JxxXyn4t/YX+MXwr/YH+D/7O8/wysviVqHw98VGXXdH0e98m1vbcm6dXBnVCyAyxeZGQQx3oSy5avv+isa9FV6Tg3bbVeTuvyPQyvMJ5XjY4iMFKykmpXs1KLi07NPaT2aaPzL+LfiD/gr3eaRe+Cv2Yv2U7b4aeFykz6fo+kSWcdwkrq75kucNwZJVA8qOM4hbPmGRgv0/8BPiX8bvhN8IdF8B69+zH8Tddv7O2MmoavqOo6c0t1cSM0krnzb93GXcnDMx5r6VorH6viP+f0vuj/8AIno/2vlP/Qup/wDgdf8A+WnwJ+11oP7SXxe+MOjfFXw1+wbrPil9P8LzaXp+l+OdetItPsLhp97Xv2e3kkW5d8QRkOVTYhOCPlPzh8QPgb/wWC+Ll5ZaH40/ZjjTw/b3wlt9KsvFEENtYxvgO0VutssQC+bNhDg/ey48xtv7FUUfV8R/z+l90f8A5EP7Xyn/AKF1P/wOv/8ALT5i/Zv1nxd+zZ8JNL+FHhf9jLx/IljDuvL43GkpJeXB/wBZMwF51NfI3/BRH9m39uz9pj9rZPjv8IP2Rrv+yZ/h9pPh+6t/Efii2tLiK4tL/V7l3RYFuBJG0eoIMeZGcoe2K/Vaij6viP8An9L7o/8AyIf2vlP/AELqf/gdf/5afixoX/BPX/govdSW6eJ/2W4LRTNGLtrPxTDdlEH8S+bHH5h/3sD3r71/Zb0vVP2Uvh1H4N8F/sWfEC4u5gJdV1mafSfOvZehyRecRjPyR5wB0z1r6yoo+r4j/n9L7o//ACIf2vlP/Qup/wDgdf8A+WnkP/DSPxZ/6Mv+IP8A4GaT/wDJlcl8efiZ8bfi98F/FHwu0T9j7xxa3niDQ7iwtrm8vtLEUTyoVDOVuyQoJycA19F0VNTCVqtNwlWlZq20ev8A26dGE4hy/A4qniaWX0lOElJe9WesXdf8ve6ON/Z3/wCTf/A3/Yn6X/6SRV2Vcb+zv/yb/wCBv+xP0v8A9JIq7Ku4+WCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiim+YPM8vvQA6iiigAooooA4j9nCaG4/Z58BzW8weN/Bel+XJH3H2SKu3rzv9lDUbfVv2XPhvqtlOJY7nwHo8kUuf9bmxi5r0SgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKwfEHhYa7r+g6ymv6pZjR76W5a1sboxw3oe2li8q5jAxLGDL5oHGJYoz2xW9RQBheNvCi+OPBep+Ev7c1TSf7Ss5IP7Q0O8+zXtt5n/LSKXH7uQZyD61u0UUAFZOsaEdbns521a+tvsV3HcAWV2YxNj/AJZydpIz6VrUUAZut6bDq2nTaRNc3MUd1FJHJLaTSRypx1SSM5jNO0bTDpWl22mm+ubo20Mcf2i9m8yWXAHzuSOX960KKACsXXPDdrrdxYTzaheW/wDZ96LqMW1zJGJT5cqeXLj/AFkf7z/VnuB6VtUUAZ+taRb69pN1otxO8cd1ayQSSRyFHXzBjj0NVvCvhjTPCOkx6XpUTnODNNIMySyf89JD36Vs0UAFFFFABRRRQB5p+xxLLdfskfCu4ml3ySfDjQnlPrmwhP8AOvS68F/4JzfFiw+K/wCxf4A1PSNKvrI6PoEOhXUWoiLzDc2EYtZn/dSyDYZIjjmveqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPG/8Agnj/AMmHfBb/ALJL4d/9N0NeyV5J+wTFBbfsOfB2CH7kfwt0HH/gvhr1ugAooooAKKKKACiivmb44/8ABT74afBD4keKvh1e/BzxvrJ8Gm3GvavpFjA1pbmeNJEy7SgjO/HzAZKtjIGa5sVjMNgoKdeXKnp+F/yTPbyHhzO+JsVLDZZRdWcVzNJpWXMo31aWspRS6ttH0zRXxB/w/a/Zv/6JJ43/AO/Nn/8AH6P+H7X7N/8A0STxv/35s/8A4/Xm/wCsmR/8/wBfj/kfaf8AEF/FH/oV1P8AyX/5I+36K+Of+Hy3wnGjajr8v7PvxCjtNJMI1KWa1s0+z+dgxbg1wCN4IK8cjpWN/wAP2v2b/wDoknjf/vzZ/wDx+nLiHJY71kvv/wAjOn4PeJda/s8tm7aOzg7Oyevvdmn6NM+36K+JLP8A4Lm/s96jeRafp/wa8dz3E8ixwQQ21ozyOxwqqBPkkkgADrWr44/4LL/Cr4Z6yvh74h/s6/EnRL54FnS01TTbaGRo2zhwGnBIJBGfUEdQaa4hyZx5lWVu+v8AkKXg/wCJUaypPLZqbTaV4XaW7S5rtK6v2Psaivj+5/4LCfDuz8H2fxBu/wBmb4nx6HqE/k2OrPpNuLeeQkgKr+dgkkED1IIHQ10EX/BT7wxp/i3w14W8dfsy/E7w0nirV7fTtL1HW9GhigaWZ1RTkzcgbgSBk4yQDWkc8yqW1Xt0fXbp1OOr4XceUVeeCf2vtU73hrJJc1249UtV1PqCiisLWdW1jSdW0q30/wAMXt9FqF68d7cwzR+Xp8fkySeZIJJMkb40jwmeZRXqnwJu0Vh+KNcvtA8MXus2fh281m5srSSaHTNO8s3F26AkRxeaY495PTJFblABRRWJr+qahocVoLDw5c35ubyOCf7I8Y+zRnrMd+MpH/XpQBt0VXvJZbe2e4ihkleOPIij/wCWhqHR7y41LSbW/udOksZZYUeW1lxviPXyz/KgC9RRWZrGpajp8totnolxeC4uhHKYyn+jIc/vTntQBp0VR1O7nsdLnv7aykupIoXkjtYxh5Dj/Vj8eKl027nvLGG8ubN7d5Yw0kUh5jPoaALNFFfHn7Sn/BTf4jfA74k+N/DGgfs8WOqaF4Iu7O1vNevvFa2huJ7i3SZY44jES74ZvlQsdqbjjIFceNx+Gy+mqld2T00TfRvon0TPo+GeFM84vxssJldNTnFczTnCCs5RgtZyim3KUUkndt6I+w6K/Nz/AIf2+Kf+jYrLpn/kaX6ev/HtQP8Agvb4pKlx+zFZFQQCR4pfAP8A4DV4/wDrbkP/AD9/8ll/kfov/Evvix/0AL/wdR/+WH1J/wAE0PiZonxL/Yl8ASaRaXcT+HPDtp4dvYruKMYurCGO1m8vyjgx+ZEef8K9/rx/9hqxtbb9i/4TzWVlHELr4daNc3nkxbPMllsIZJJMf89DJk/jXsFfSH4uFFFFABRRRQAV+a37YHxK+Fdv8cPF/wAKfiPNogtb747eHbjW4LmJVuG0oabD58hkxkRgjaT2DsP4q/Smvhj4/wDh34KeENG/aN/aX+IX7OOjePtZ8I67DJp2n38cgkucaVZEQBo45CBlieAev414md4avi6dOlSaTcnvtbllfY/UPC/O8r4ex2Mx+PjNwhSVvZ2U1L21Llacmlo9e/Y+ZvBGu/BDx5rviDVPD/gv4Y2r2/ir7De2Wvqlraw+FE3AT2pZlMlyed8yFrk4jKDlswan4m/ZCtPCR+H3g7RfDTaXP4F1nUV1nVLbfqratDeXP9nxNKcGMtEkR8oKNwkw27jHufw40/8AYp/aW+LGmN8Jf2ePDdt4b0T42t4X+3DTZEXXLb/hGIdS3vDPFGyAT3Rj2kHm3Vv4q+v/APhhn9jj/o2TwT/4T8H/AMTXzdPh7HzcoXp3jo3Z9bPTTTTRn7PjfGLhPDQoYjkxfJUXPGKnFW5HOmub3mpNNc0draXu9T8/fHfjT9mT4kfEvV/Gnxo8Q6Umg62PCLadHoN8I3ntFgZLwSxwEspWRESQMBIqIAuCFNV/jD4l/ZN8G6vr/ijTfhj4Qj8S2HgoyaJps19Z31lPcte26QP5NmPs7SLAZXC5LMigyL3b9Cv+GGf2OP8Ao2TwT/4T8H/xNH/DDP7HH/Rsngn/AMJ+D/4muqXD2YSjLWnd3d7Nu7bfbo3p28zwqHjFwhQqUrU8X7Omox5FOMY8sYwjaymlaSh+8unzNprltZ/jp+yt458NeEv2k9A8Z+Mr2202z+2z5vfIAhsZpYZEim2jhUjldG/2Que1dn8VfCRu/hH4V+EPib4u+FrnxNoEur6veXEviFLiKGzmMHlWyXEe9ZJXaOSURA5w4OMtiv1Y/wCGGf2OP+jZPBP/AIT8H/xNH/DDP7HH/Rsngn/wn4P/AImuKlwhjoYd0nUi07/zdeW/T+6rfP5fTY36RPC2KzinmFPCV4Sgoq37t6xVRR1b0Vqs+Zdfd1VmpfnX+0F4s+C3xV8BSXVl4p8PaFoN5pOiWPg6+stcmbUZLiJII54NQs0ZglvAEkbJiQKURo2cyHfQOoiK++CHgXx9rvh2TxJ4c+IenW2hWfhHXRe28mlNcRNJdXOySSNZ5JgpDZV3XO5dqpj6m+G/7Mv7KPjn9sT4p6hP8H/CFv4Q8B2+h+C7CwnsYIraTxFcKdRvWKFdkkvkX+iwxHOctNHjJNe1fC79nH9iPxFbaf8AFD4SfBnwHdx29+z6brOk6PAwS4t53RmRtvDJNE6gjoy8HiuyXDWPqVXOUoXdr2v3vtbvt263PnqXjZwrhcDHDUqGIcYOTjzODabp+zXvczaTj/E0fM23FQTse0UUUV9sfzAFFFFABRRRQAV8P/tX/twftmfCj4gfEG7+Hfh/wnF4P8IazYaTp02p6HeXFzqN5cW0UpiVopNmQXPLbB8yKMsa+4K/L39sf466r8Mf2lPF1pceC9cvtK0P4zeH/El9qFvEWsvKhsLeMWzMcqJHdo9oY4LEd8V8/wAR4iph8JBwm43la6/wv8L6n694NZPg844gxEMRhqddQpKXLUtayq0k7XaSk4txTd7KTsrkA/4Kw/8ABRlrqexX4NaMZ7XSxqVzD/wht9visiMi5YedlYsfxn5feq9p/wAFdv8AgoLfeGpPGdl8LPD82kQyIkuqR+E7xrdGdiqgyCfaCWBA55PHWuC+Ff7SF74617W31uL4gjUD46bxba3PhG3N1dX8asQun3HzApEpcbH+dEMjjyzuFbGu/HL4u6/bppx/Z08cWGm/8IDrmjHQrDRp0sob2+uriaOZI1QKVjWSJSxXcfLz2FfErH49x5vrVT7vO2vb9eh/Tz4T4SVb2ayHCuzjd8ytblbdrv3m2klZ+7f3tdD1L4hf8FJ/29vhMmuaf8SLHwFpmr6NDYyppT+H7tmvI7pA6NG/ngfKCQc91YDJU45ub/gq5/wUct/E6eCZ/gxo6axJDHMmlN4NvvtDRvjY4j87dtORg4wazYP2gPGGj+Nbv4k/8M3+Oddv9Yl8M3d1p+q+GpRFZS6bE8Uscbsrl92RJG+0YbBK5FO+Lf7RPxa1jT/EWheC/hR46mfVfCEmm2GtQeBDpUttLPewXEqbIt7BfLiZWYSHe8jEKoyW3qY7FuLlHFTVr2WrbV3bXu1bZaddzzMHwtkCqU6NTIsLJyUeabajGMnGmp2SvaMJuWkpXmtYNpFzwF/wVy/by+JHxAsPhl4b8JeCDq1/efZkhuNGuIxEwzvaQm4+RUAZmJ6BTXM+J/20NK1zV7nTPiz410vXTefGbRNf10eH9Mm/sy7063tUSby1mG8gNHGCh5YqSOK8h+A3h/4z/CP4v6T8R9X+Bfi7Uba2llTUbZdDuRJNBNE8M21in39kjEE/xAZq43gTQ7qHUPhtYfDL4h6lo+j39zd2Wp2vhryr5rqa3tgkFzEQ4iRfLJ4YnEgYD18xZjmlagvaVG3d6SvtZW087u712XfX7aXBnA2X5rJYPCU6dP2cHzUeRPm525Xk7tcrVLljo3zN3fL7vofgn9qbwj4o13xBrmsfEmw8MXjeK/Omur7w2LiPVPC6bgmjQxpGwQKCf3LBEkDjc42CoNU/az+DEnhQ/Dvwppp0fwrN4E1qNtBTSVbbrL3lxLp7ySEFnaNDBh9xVDnGOa5rUPHPwX8O+G9Q8J66/wAQbi68SCysda0zxFpkUv8AwjMccsbzSWxkkzcTgIY4srDhHO45+UwfF34l/AuDx54L1z4ARardaL4U1OCS08Lat4ejtI/LjlWR2muEmle5mlZf3kjKAAcKqoqxjSM8c42jJNvTZ3d5eu1rXtvezV7tcdTDcL067qVKU4wjeSfNFQj7Ok0tVC/NzOSp891HlcqclFxjP9Yf2IAI/wBjD4RKf+iZaD/6b4a9Tryz9iVJYv2NPhLDNEI5B8MtBBj9D/Z8PFep1+vH+dgUUUUAFFFFABXwP+214jXQfgj8ftMH/Mb+K+kWX5adY3H/ALRr74r461z4NaJ8efj54r8H+NL62i0DSfjBBrWuJPMFFwsOi2ccduAef3kkoBP/ADzWUVx4lpV6N/5n/wCkyPo8mjKeVZioq/7qH/p+ked/sSfs2+IP2fvBfwr1zxTqIlvPiL8Z5/Ff2D7Ps+wRy+GRaRQ5H3/3VjHJn/priv0Jrxj9oXWNI1H4u/BiLT9Utp2X4gTFlhnViB/ZV9zwa9nowzTr1rfzL/0mIZzGUMqy5SVv3c//AE/VCiiiuw+cCsjxP4r0PwN4X1Pxn4r1OKz03R7CW81K7k4jihijMkkv0AyfwrXrgP2i/hJP8e/gp4g+DUfiP+zLXxFBHY6vc/ZhJ5lhJLH9rtvLPXzbbzIf+2tAHy78N4/iH8E/2G7X9ou/0m1sPG11b6z8Tb7QtXtD5t14m8Qi6/szSpJMD97Eb6Ow78RRR5/5aD1n4O+Ofh5+yh+z38Kfgf4euv8AhIbc63YfDrw5e6VLHJLqd/axzRXt1IOkflfYb65m5kP7mSt74/2dl4z8dfD/AOD+hWdtcRz+JT4l1uyjnijJttGjjmtj/wBs9Tk0c47AVmyeGtL8Wftt+H/D2mxQQ6X8K/Bl1r13amEDztZ12eW1tboHj95Ha2OriT1/tAUAe+UUUUAFFFFABRRRQAV8neNvgIf2oPCf7RXwUt9aXT7jU/H2mzW11KuY0mtrPTbqIyADOzzIFz7V9Y15D+zh/wAlf+NP/Y/W/wD6abGuPEf7xR/xP/0mR9DlH/IpzH/r1D/0/SPyr8Ja38ZP2Iv2iLPWtX0O40rXvD14Dd6fdH91dRf6qSMH/lrFLF/y2r9ifhR8S/CHxk+H2l/E3wTfJdabrFqZrWRR2Jw6fUOCPwrkP2nv2S/hf+1T4MHhzxrYiC+tBnSNbtIR9psZOehJ5j5GYzxXyX+yV49+KP7CHxsn/Zo+LmqWd94L1TV/s+ma1FL/AKNbX37nIMh4izD+88o88x9K7D54/QyimRTJMgljPymn0AFfOvhT4w6H8Cfjb8VLHx/4S8Wj+2vFlvf6ZcaX4Pvr2GeD+zbSPeskETr9+NxjOQVr6KormxFGdVxlCVnF32v0a7ruexlOZYbAwr0sRSdSFWKi0pcjVpxmmnyyW8bWtsz53+Inx2/ZO+LulDQ/iX8HPFGvW0X+qh1L4ValL5XH/LMm3yn4V8SaN+yp+zf8RfBd9rvhbWfiD4f1VfEeqRW9rH4A1HUbH7Ml5KsKsyQeYHWMKGDSOQQQQCDX6x15D+xR/wAkm1f/ALKD4k/9O1zXFUp4z65T/eK9pfZf93+8fT4TF8N/6t4trCVLe0o3Xto9q1tfY6demvlbXS/Y0kgm/ZC+FMsB/dn4b6F5X/gvhr0yvNP2OLX7F+yP8LLLzfM8r4b6FH5nriwh5r0uvWPgAooooAKKKKACuE8Z/swfs7fEXxFP4u8dfBXw1q2qXQUXN/faTFJLLtUKu5iMnAAHPYCu7orOpSpVo2qRTXmrnVg8djsvquphasqcmrXjJxdu101pocJ4M/Zf/Z1+HfiKDxd4G+CnhrStUtQ32a/sdJijli3KVbawGRkEjjsTXd0UUU6VKjG1OKS8lYMZj8dmFVVMVVlUkla8pOTt2u29NQooorQ5QooooA8T+FmgfFnU/wBqT4geKPGvgnR9J0ywe2svDmr20d28usWMttHJj99+7ikilj/eSxf63zBFJF/osM0t39l22/tyDxn8bZZCZPHnje5vLQDnbYWoi0yyMffypYbEXX1u5K9fqOGKGCPyYotiJ0A6UASUUVjX2g6RqOo6de3vmiTTbz7VZ7LuVD5nkSQ/OMjzP3cj8Pkd+qZoA2aKx/FPh7TvFHh+/wDDWrJPLb3tpLb3SwXstvIY5BggSxESR5GRlDWxQAUUVm61oGla1BbRalb71tbuO5gxIUxLGcpQBpV5D+zh/wAlf+NP/Y/W/wD6abGvV7uygvbSSzuOY5Iikn0rxX9lLRtM8MfEL4u+HdLikS1sfGVnBbRySvJtjj0ixABd+Txjk81x4j/eKP8Aif8A6TI+hyj/AJFOY/8AXqH/AKfpHuNfP/7Y/wCzHq/xM0mX4h/CXRNMufFdt5R1HSdWhH2HxHaxf8ulyB/y0/55Tf8ALKvoCs/U9G0zV/s/9pQBvst1HcQnzOko6V2Hzx4F+xJ8dNA+JXhTWLPRf7Qj1G016Ua54T1mcfbfDp6eTHnPm2sfl4ix619G14F+0V+xlpnxS8UL8W/hf4wuvB/jVLOa3k1PT4f3eqRSxeV5NzgeZ3GJBzHz1xXI/si/tPXfgmyf9mj9rXVf7A8X+HLe2j0yXVZsDVLCWKIxHzR+7lmi/wBXLj6/89MAH1XRVdLoSXb2hhceWA3mHpVigAryH9ij/kk2r/8AZQfEn/p2ua9eryH9ij/kk2r/APZQfEn/AKdrmuOp/v1P/DL84n0OD/5JjGf9fKH/AKTWNb9kKeef9k34X3FxbmJ5Ph7opkic/wCr/wBAi4r0ivOP2RhMf2T/AIYeeAjn4faNkR/9eEVej12HzwUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeQ/s4f8AJX/jT/2P1v8A+mmxr16vIf2cP+Sv/Gn/ALH63/8ATTY1x4j/AHij/if/AKTI+hyj/kU5j/16h/6fpHr1FFFdh88FeC/ty/skWn7TXw3F14ct7W28ZaMDLoGoSxY8wf8ALS1kk/55S9DXvVFAHxt+w/8At1an4z8aW/7P3xihmsNdt4fscM90xEst1FmLypfM/wBXLiM/9dTz1/1v2TXy5+1b+wTpPxP8XXfx7+GszWfjO10fzLK2tG8pLq+jmSWKUyevEg+vl+lSfAj9tqLVGv8Awf8AHK0tvD/iPQr0ReIEn8qMRQ5PlzD97+9j5/5Z/wCr/wBZ/qjmgD6fryH9ij/kk2r/APZQfEn/AKdrmu90PxXFrWp38VkY5Ftbz7OVg5MfH+sevP8A9imaMfCnV4TIN/8AwsDxJx/3FrmuOp/v1P8Awy/OJ9Dg/wDkmMZ/18of+k1jkv8Aglp8PpPh7+xJ4Mhh8R3F7a6vZyatYRXf/LhbXR82O0i/eyYjjBwOa+jq+E/+CVf7Zfg2z+A3/Cofjh8TPA/hx/BUFrZaGLnUY9Omu7HypMSyRzTYkf8AdHMkXBzn1z9Vf8Nafsrf9HM/D/8A8LOw/wDjtdh88ehUV57/AMNafsrf9HM/D/8A8LOw/wDjtH/DWn7K3/RzPw//APCzsP8A47QB6FRXnv8Aw1p+yt/0cz8P/wDws7D/AOO1xvib/gpN/wAE7fBV1/ZvjD9vv4MaTc+XHIINS+KWlW8hjOef3lyM/ck/L8wD3Sivnz/h7N/wSx/6SWfAH/w8Gi//ACVWf4j/AOCr/wDwTXOj3UXhL/goV8Dr3VPskh06xs/ino9zNc3JB8qOOOO6zLIZMDYOTmgD6Tor4H/4Jnf8FEv2W/Bn7IWg+Af2mf24vh5pXjPSNW1iy1HT/GfxMij1JYxqt39l83+1buW6P+jeSR5skhEQj57V9GWH/BR7/gnxqYkFl+3X8HLgRf63yPibpcmPymoA9sorxj/h4h+wJ5vk/wDDcPwnz/2UTTP/AI9T/wDh4d+wN/0fB8Iv/Dk6X/8AHqAPZKK8b/4eHfsDf9HwfCL/AMOTpf8A8eo/4eHfsDf9HwfCL/w5Ol//AB6gD2SivFLj/go1+wBZQ+dcftsfCn28r4g6fJ/KWuF+LH/BVH9h7S/hNrWvfDT9sDwDd+IJfDd1ceG7eHXYZJZLvyZfs0Zjz180f6uSgD6lor4d/Zr/AOCxf7JOj/BbRLH9q79q3Srfx8huk1+KfSJo5QftMoi3x2tt5S/ufK6D19a7/wD4fQ/8Ewf+jvNC/wDBdff/ABigD6jor5c/4fQ/8Ewf+jvNC/8ABdff/GKj/wCH0/8AwTA/6O80P/wXX3/xmgD6nor5c/4fQ/8ABMH/AKO80L/wXX3/AMYrh/Fv/Bdv9hjRfE48P+DovGfjyxFpBc/294K8MC6s45ZHl/0UiZ4pPOj8tHI8vpLFzzwAfbdeQ/s4f8lf+NP/AGP1v/6abGvCPBH/AAXN/Ys8TazLZeK7fxt4N0uG08+bxB4z8N/YbaMnyvLhH72SWWSTzTgRxn/VSc8V5X8Uf+CmH7N3hm4+JXij9mv/AIKDeEILnxckuoWWmTeCNQuL1b9NPSCOO3ncLCsjGFNvmoQGbkMBzx4pVFOnOMXKzd7W/la6tdz6PIZYSeGxmGrVo0nUpxUXLms2qtOVvdjJrSLe1tNz9KaK/Nf4Sf8ABUSbwf8ACLQbf4//APBQ3wBp3ifZLb38PiTwDcTanI0b8STiwaGJC6bCD5MQwQMZGa7C1/4KtfCu8DfZ/wDgpd8GBjr5nw/1NP53FH1qp/z5l/5L/wDJE/2Hg/8AoYUP/K3/AMpPviivhfTv+Ck3w/1S9gsbX/gpz8BlluojLC154euYo/xeS9AT8cVvad+3Z4d1KF5IP+CpX7NUYil2E3bQxc/9tNTFH1qp/wA+Zf8Akv8A8kH9h4P/AKGFD/yt/wDKT7Kr5y/bG/Yp8O/GowfE7wrqbaT4m0O0kMBjtfNivovMlllhlij5l83zJPxP1riv+G3dG/6Sp/sw/wDgba//AC0pkX7cOhyl9v8AwVR/ZlPl9f8ASLb/AOWlH1qp/wA+Zf8Akv8A8kH9h4P/AKGFD/yt/wDKR/7D3x81PTfEEnwU+MGp3+m6tZ2Ekvh2XWJjHFqel+b5cXQ+VLLF5ckfmRdRx+88vNdp+zXq/iPQ/hHcXfhTTobq6m+JPiSGeBYzlIzqV2d/314XYV/4F1Tk182ftJfF/wCFPj3RItS1D/goL8BLrWtJuDd6Bd+GbVvtVvcyjzPLje3vJADK3P7xSC8nqa4/wN/wUNtfht+xtq3gn4b+Okh+IV5r01/aawvhqWVHkn1FPtMsPnRGIMkLSMJJezj/AKZebEZVa2LhJ03FJS3t15ezfY6q1LA5fkFehHFU6s6lSk0oe02iqt2+aEV9pdb67H//2Q==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8dHcni6CJYt"
      },
      "source": [
        "# Conclusion and Next Steps\n",
        "\n",
        "Congratulations! You've trained a custom YOLOv5 model to recognize your custom objects.\n",
        "\n",
        "To improve you model's performance, we recommend first interating on your datasets coverage and quality. See this guide for [model performance improvement](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results).\n",
        "\n",
        "To deploy your model to an application, see this guide on [exporting your model to deployment destinations](https://github.com/ultralytics/yolov5/issues/251).\n",
        "\n",
        "Once your model is in production, you will want to continually iterate and improve on your dataset and model via [active learning](https://blog.roboflow.com/what-is-active-learning/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7iiObB2WCMh6",
        "outputId": "33d06aef-ae60-498f-978b-f7359f63499f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "#export your model's weights for future use\n",
        "from google.colab import files\n",
        "files.download('./runs/train/exp/weights/best.pt')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_1e3d951f-d6e8-47bb-8a9f-582f2680e91c\", \"best.pt\", 3727293)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}